{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "okHucoMFSm_4"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CA7NTWIuUNOj"
      },
      "outputs": [],
      "source": [
        "#@title Setup\n",
        "!pip install git+https://github.com/google-research/google-research.git#subdirectory=sd_gym\n",
        "!pip install dm-acme[jax,tf]\n",
        "!pip install dm-sonnet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LIiY-smm-0rp"
      },
      "source": [
        "### Download sample SD model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "executionInfo": {
          "elapsed": 640,
          "status": "ok",
          "timestamp": 1698183330611,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "dwkhYdn4jikf",
        "outputId": "c0d158fe-d0f3-4e48-fa63-de51987b8dcb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  100k    0  100k    0     0   172k      0 --:--:-- --:--:-- --:--:--  172k\n"
          ]
        }
      ],
      "source": [
        "# Interactive model here: https://exchange.iseesystems.com/models/player/mindsproject/electric-vehicles-in-norway\n",
        "!curl https://exchange.iseesystems.com/model/mindsproject/electric-vehicles-in-norway -o /tmp/electric_vehicles_norway.stmx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a3IhnNnWkc_J"
      },
      "outputs": [],
      "source": [
        "# PySD doesn't support random, so replace with a fixed value\n",
        "!sed -i 's/RANDOM(0.98, 1.02)/0.99/g' /tmp/electric_vehicles_norway.stmx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZJK0FHgQIf40"
      },
      "outputs": [],
      "source": [
        "# Needed for BPTK\n",
        "!mkdir /tmp/generated_sd_models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HS4GRecXpWDR"
      },
      "source": [
        "# Demo of SDGym"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWSu3IW9paEY"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3iuvPPfck0Ab"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sd_gym import core\n",
        "from sd_gym import env as env_lib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TfOulRgMoQ4V"
      },
      "outputs": [],
      "source": [
        "sd_model_filename = \"/tmp/electric_vehicles_norway.stmx\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wz6aQMYsjqLE"
      },
      "source": [
        "### BPTK Simulator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_mqRBfhyhSb3"
      },
      "outputs": [],
      "source": [
        "bptk_params = core.Params(sd_model_filename,\n",
        "                          env_dt=1.0,\n",
        "                          sd_dt=.1,\n",
        "                        #  sd_units_types={'years': np.float64},\n",
        "                        #  default_sd_var_limits=(10, 20),\n",
        "                        #  default_sd_units_limits={\"years\": (1, 2020)},\n",
        "                        #  sd_var_limits_override={\"averageCarLifetime\": (30, 40)},\n",
        "                        #  actionables=['chargingStations'],\n",
        "                        #  observables=['pcInUse'],\n",
        "                        #  initial_conditions_override={'ecInUse': 200},\n",
        "                        #  categorical_sd_vars={\"averageCarLifetime\": [10, 15, 30, 45]},\n",
        "                          parameterize_action_space=True,\n",
        "                          simulator=\"BPTK_Py\")\n",
        "bptk_sd_env = env_lib.SDEnv(bptk_params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HrTRTzxwxfwu"
      },
      "outputs": [],
      "source": [
        "bptk_parameterized_no_action = {k: (0, v[1]) for k, v in dict(bptk_sd_env.action_space.sample()).items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7HJvWuyEF_O8"
      },
      "outputs": [],
      "source": [
        "obs = bptk_sd_env.reset()\n",
        "\n",
        "done = False\n",
        "while not done:\n",
        "    # obs, rew, done, info = bptk_sd_env.step(bptk_sd_env.action_space.sample())\n",
        "    obs, rew, done, info = bptk_sd_env.step(bptk_parameterized_no_action)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_RQi21Njse9"
      },
      "source": [
        "### PySD Simulator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ztcyAYRaoSgl"
      },
      "outputs": [],
      "source": [
        "pysd_params = core.Params(sd_model_filename,\n",
        "                          env_dt=1.0,\n",
        "                          sd_dt=.1,\n",
        "                        #  sd_units_types={'years': np.float64},\n",
        "                        #  default_sd_var_limits=(10, 20),\n",
        "                        #  default_sd_units_limits={\"years\": (1, 2020)},\n",
        "                        #  sd_var_limits_override={\"average_car_lifetime\": (30, 40)},\n",
        "                        #  actionables=['charging_stations'],\n",
        "                        #  observables=['pc_in_use'],\n",
        "                        #  initial_conditions_override={'ec_in_use': 200},\n",
        "                        #  categorical_sd_vars={\"average_car_lifetime\": [10, 15, 30, 45]},\n",
        "                          parameterize_action_space=True,\n",
        "                          simulator=\"PySD\")\n",
        "pysd_sd_env = env_lib.SDEnv(pysd_params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P0w2pgXoGWi5"
      },
      "outputs": [],
      "source": [
        "pysd_parameterized_no_action = {k: (0, v[1]) for k, v  in dict(pysd_sd_env.action_space.sample()).items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QflVr1GhluZj"
      },
      "outputs": [],
      "source": [
        "obs = pysd_sd_env.reset()\n",
        "\n",
        "done = False\n",
        "while not done:\n",
        "    # obs, rew, done, info = pysd_sd_env.step(pysd_sd_env.action_space.sample())\n",
        "    obs, rew, done, info = pysd_sd_env.step(pysd_parameterized_no_action)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rVyKorboLUjK"
      },
      "source": [
        "## Compare trajectories between PySD and BPTK"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "coATytULwyj5"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams[\"figure.figsize\"] = [15, 3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "height": 929
        },
        "executionInfo": {
          "elapsed": 870,
          "status": "ok",
          "timestamp": 1698183371384,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "GQ1z-2mJj6dZ",
        "outputId": "9a6d56dc-82f5-470a-8c65-f8a139c7a027"
      },
      "outputs": [
        {
          "data": {
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pw/82tHmU0v+7dLvuJ24vI1zywGSdP0cabjvOoXLaFdkYNkrxv53R2uCBBe+f8pR2T9J1\nXF4YtO1n7DY0HHTdIe38SDpCxz+Ufszb6fet3bA+rn9mvsz9qFz/+zxW+c/IOP4p7fv+f555HvsQ\ngAAE8iVAz1y+5DgPAhCYbAJf0wX/SPorvSQdr9g9DH4p91/A/YL65jwq5Jcqv0hfpzKvVNwneUjb\n0ZINTqKgFzivwfW3yvwFyd8BfV+xXybPk/xy2SqN5a/7o15X13s0qq/Lv1Hbvo/50gWSDc9xaddz\nPdYqj3tCfis9LfkF/VXS+yW/QF8QvYRqM3hMcs/CR3TOHsXt0jYdv0nxf0h/LN2kYzYbNklnScMv\nztoOQ8TkGW2eo7zu6dkqdSs9NqJRzuH8TyrfP2rvk9Kt2v6x4lmS73Gm9F6dG5vv/c7NsXOajl+p\n8n6q+EnJL+bm82HpLpXptDhcqI1bpDuV/98U2/C1SDYwJ0k/VH4b93zD/9aJayW3kX9R3C6dIx3w\n/2Jd5z+U59917ELFr1H8K8ltcpnkurxSOkTKGVTWIyrj3cpo8/Kgtq9RfJdkg3eQdKZ0jPQ2yc/O\nSx84j+s2W9u+9iLJ7cv/5j7qfHkEt7ePqTy3V7eNfulU6a3SNbqu6+Nwj2Sz9ZfKayPWLa3Xcdc5\nV/iGMnxfchv9nuIW6S+k5yXfQ3r4K+3YwNrknq74dsn/No6V/EzeF2W+U7HbxjeV79eKXW+3nfXR\n8czo00rw78h/Kv83Ffte3yD9iXSr9J3ME9iHAAQgkC+BA/4Hkm9BnAcBCEBgIgnoxakveuH6uK7z\nHukLkl8sbU780jvmoDL9Um1z8tfSZyW/TN4o+QXTL12Jg8r6osrq0AkXSZdLL0g2J345bJXiF9XE\nZebI+BEd3yx9SPqSZFNyvnSCZLMSX69d2x+U/LJqU7JYmi5tl2xcvq6636w4DH6h1n34Jf5z0j9J\n9ZLz3aRjV+uYDdbHJF+zTfql9Clpp5QZ3quEr0p+VjZ8fqF2/qxB5XuRar/4+t7MsE/yC/x7dOy2\nrCflTnxQWX4mrZFcn2rJz8Z1+rI0HHQNL1Zudn4Zf5fkl3+3sUcls71yOHMeGyrfa7T5Jd/3ZmZu\nLz+RrpAezixS+T+o/H4250mXSnXSS9J90X7mKSPuq6xfq6zDleFi6QzJ91cruQ3ZxFysPGsVx8Gs\nfJ1zJXOyoXI7+GvlO6Cu8Uk54rU6fqz0Dsnt0ObchugT0jekMKj8F1RXt9lLJLNxPb8juS2MGnTu\nD3TuEmWy+fqK9Jz0d5LN4WvTT1beNuU9SWnx836ntjulx6Svp+X9kbZd73Okd0vTpD+XXPcDgsp9\nXuX6Wr7un0kt0kbpi9LndHzggJNIgAAEIJAngXiNmjxP5zQIQAACEBiNgF7q/FLoF8sleonbMlre\nQhzT9X6pct4oNel6flkmQAACEIAABCBQpgT81yUCBCAAAQiMk4BM1PTMIpTm3of3S48U2sip7IYs\n1ztaaW+T3IuGkcsExD4EIAABCECgzAgwzLLMHii3AwEITBmBNTJYHlbpIX0eUrVS+gtppuQhdYUO\nXk7ARvHXkodMvkrycDwPTfybQl+M8iAAAQhAAAIQKD4CmLnieybUCAIQKE0C/tbrWckGbq7UI62T\nvqheshsn4JbuU5n+xuej0hzJ3/rcJP2trnf/BFyPIiEAAQhAAAIQKDICfDNXZA+E6kAAAhCAAAQg\nAAEIQAACEEhCgG/mklAiDwQgAAEIQAACEIAABCAAgSIjUPTDLOfNm7dv5cqVRYaN6kAAAhCAAAQg\nAAEIQAACEJgcAvfee+8OfUYxP/NqRW/mbOTWrfNnJwQIQAACEIAABCAAAQhAAAKVR0CTnj2f7a4Z\nZpmNCmkQgAAEIAABCEAAAhCAAASKnEBOMycXuFy6WXpcelS6yPek+DJpk/RApLfH96r9S6VnpCel\nt6alH6f9h6Nj/6y4qsj5UD0IQAACEIAABCAAAQhAAAJFSSDJMMsB1fzjGqN5n7zXLG3fq/iG6G6+\nqvQvpd+Zjh2h/XOkI6Ul0o1KOzRawPYK7XsdpDul66QzpN9IBAhAAAIQgAAEIAABCEAAAlNKYOue\n3mBWbU3QWFs9pfVIevGcZk4mbIsKswJtd8qYPa7NpaNc4Ewdu1p5exWvdy+c4hMUb1DcpPQ7fK72\nv6voLAkzZyAECEAAAhCAAAQgAAEIQGDSCAwMDQVPte0JHtrRJXWG8ba9fcEXXvfK4M0rvGRs8Yec\nZi79FmTAVmr/WOku6WTpQqW9X7FnKHHvXZtiGz33vMVhY5TWr9jbmelpSWxCAAIQgAAEIAABCEAA\nAhAoPIG2nv7g4Z1dwcORcXtsV3fQOzh0wIVs6srOzMm0zdSd/lS6WKatQ/seMvlZaV8Uf1nxB6Vs\n38E5z0jpOrR/UNkeimkFK1asyDzMPgQgAAEIQAACEIAABCAAgREJDO3bF6zfvfflXjeZuBc7e0bM\nn37AZq9UQqKeOZmrWt2QjdwPZOR+5ptTvDW+SR3/V23/Ktp379vy+JjiZdJmyenejkOcnpaU2lTZ\nV2rLClpbW20ECRCAAAQgAAEIQAACEIAABLIS6OofCB7d2T08XPIRmbfu/sGseUdLrJtWFUyvqQ5s\nBqeVwFyNOc2cjJp71L4lPS6T9ZX45pW8WPvht3QK75QeibavVfxDHXdeT4CySrpbeQeV5m/uTtS+\nh2l6eObXo3OIIAABCEAAAhCAAAQgAAEIJCKwtbs3uH97Z/BANGTy2fY94XDBsYb5DbXBq+fNCo6e\nN1OaFRzW0hjUVuec8H+sl5mw/DnNnK7sb+PeJ3lJgQeimnxa8Z9qf7Vic9sg/Q8fk2nz8gXXaPMx\nyTNhXmAj52MK50tXSQ2SJz5h8pMQCwECEIAABCAAAQhAAAIQyEZAXiJ4QUMkQ/MmOd4iMzfWUK0u\nqlUtM4aNmw3cwsY6T8w41qKKJn+V4RRz8DDLdes8vwoBAhCAAAQgAAEIQAACECh3Ah7i+Ky+d7t/\nW0do3KxdmrxkrKGpriY0bq+em+p1O2LujKBBQyhLMchw3ivf1ppZ9yQ9c5nnsA8BCEAAAhCAAAQg\nAAEIQKAgBLxEwBNaIiA2bw/KvHXm8b3bwU0N+/W6rZg1vaR73ZLAxcwloUQeCEAAAhCAAAQgAAEI\nQKAgBHoGhjRZSZd63DrCYZNeCqAnyxIBo12sVhOVHDFnZnDsglnBan3rdpR64NwTV2mh8u640p4w\n9wsBCEAAAhCAAAQgAIEpJNAno+bZJddt7QjWaeikjVz/0Ng+9WqomRYcreGSqxc0BcfOnxUcqe36\nEpqoZKLwY+YmiizlQgACEIAABCAAAQhAoAIJDMioPb6rK7hXxs0G7kH1vGVbnHs0NE111cFqmbZj\n56fM26GzG4OaaaUzy+Ro91bIY5i5QtKkLAhAAAIQgAAEIAABCFQYAU9Y8rSWBoh73h6QievWUMqx\nhHnTa1NDJiMDd0hzQ0ms8zaWe5yIvJi5iaBKmRCAAAQgAAEIQAACEChTAp4Nf33H3mHzdt+2zqCj\nzyuSJQ9LZtSHwyX9zZt735bNrC/7yUqS00meEzOXnBU5IQABCEAAAhCAAAQgUHEEbN42dvUOD5v0\nd29jXSpgQUNd0LqwKSV997ZIZo4wfgKYufEzpAQIQAACEIAABCAAAQiUFYHdvQPBPVt3B3e/tDu4\nS9qyp29M9zenviY4zuZtQXNo4Oh5GxO+xJkxc4lRkRECEIAABCAAAQhAAALlSaBfM04+rFkmbdys\nx3d1B2OZb3JWbXVo3o5Tr5vN2yFa800LXZcnrCK6K8xcET0MqgIBCEAAAhCAAAQgAIHJIOChkxs6\neoK71ft2p8zbfRo6uXcMk5Y0aqkAf+uW6n1rCla1NAbVWvuNMLkEMHOTy5urQQACEIAABCAAAQhA\nYEoItPX0a+hkx3Dv27a9yYdO1smoHa3FuY9375t0xJwZLBUwJU9x/4ti5orgIVAFCEAAAhCAAAQg\nAAEIFJqAF+t+cEdnaN787dsTbXvGdIlXqrftxEXNwQkyb6vVCzddvXGE4iKAmSuu50FtIAABCEAA\nAhCAAAQgkDeBFzt7gj9saQ/ukO7VkgFjWax7rtZ6e63Mm+UeuHmagZJQ3AQwc8X9fKgdBCAAAQhA\nAAIQgAAERiTQo+/c/L1bbOBe1BICSUN99bTgNVrnLTRwC5sDL9TNpCVJ6RVHPsxccTwHagEBCEAA\nAhCAAAQgAIFEBF5Q75t73mzgbOR6B5PPO/mq2Y2heTtB8jdwNnSE0iWAmSvdZ0fNIQABCEAAAhCA\nAAQqgEDPwGA4ZDLuffMC3kmDF+tOHzo5W0MpCeVDADNXPs+SO4EABCAAAQhAAAIQKAMCXjYg1fu2\nOzRw929P3vtWo1knV6vH7XVLWoKTFmvoJOu9lUGLGPkWMHMjs+EIBCAAAQhAAAIQgAAEJoWAJyq5\nV8sG/N7DJze3B5u6k/e+LWqsk3FrCV4n89aqb99maAFvQmUQwMxVxnPmLiEAAQhAAAIQgAAEiozA\ndq3z9nsZt9s3t2npgI6gR4YuSXDv27Hz1fsmA+fet4PpfUuCrSzzYObK8rFyUxCAAAQgAAEIQAAC\nxUZgSMMnn2jrDm7fZAPXHm4nDYvd+6ahkzZwrQuagkZ635KiK+t8mLmyfrzcHAQgAAEIQAACEIDA\nVBLYq8lLvGC3zZt74Xb09CeqTm3U+5YaPtkSrGyazrIBichVVqacZk5rTSwXku9KiyT3/V6pjzK/\npvQ52v6xtFLaIJ2t9DbFbmiXKvqQNCh9VOnXR+nHKb5KapCuky7SseRzqboQAgQgAAEIQAACEIAA\nBIqYwBZ97+ahkzZw/g6ubyjZ665nnjxlaUtwsnrfjqP3rYifcPFULaeZU1UHpI/Lc90nkzZL2/cq\nvkHxudJ/Kf1y7X9K29Yl2j5C8TnSkdIS6UalHap8NnZXSOdJd0o2c2dIv5EIEIAABCAAAQhAAAIQ\nKEkCgzJrj+7qioZPtgXP7N6b6D6qlOvIuTOCk5fMDl6vIZSrWhrpfUtEjkwxgZxmTiZsizJbgbY7\nZcwe1+ZS6UxpTVTQdxSvlS6J0q9WXk/Bs175n1F8guINipuUfodiN1T39p0lYeYMhAABCEAAAhCA\nAAQgUDIEPHzyzi27g1s3tYUzULb3uv8jd2ismRau+/Z6GTh/AzeXdd9yQyPHiARymrn0M2XAVmr/\nWOkuaWFk9GzytujYgiivjZ573uKwURtO8wBhb2empyWlNlWWe++sYMWKFQccJwECEIAABCAAAQhA\nAAKTTWCHZp/00MlbZODu0XdwSYdPLplRH7xewydPkYHzLJR11dMmu+pcr0wJJDZzMlgzxeCn0sUy\nbx3aHwlJtgMeKDxS+gHlqPwrlWgFra2tyQYZH1AKCRCAAAQgAAEIQAACEMifgN5Jgw0dPWHvmw3c\nozu7giQvppq7JDhaC3efop43i6UD8n8GnDk6gURmTsatVsXYyP1AjfpnUZFblb446pVbrLRtUbp7\n3zxpShyWaWOz5HRvZ6anJbEJAQhAAAIQgAAEIACBqSPg798e2tEZGrhb1Qv3YmdPosrM0lIBnnnS\n5s1xc32i1+xEZZMJAiMRyNnKZNjco/Yt6XEZt6+kFXSttj8gXR7Fv4iOOf2HOs15PQHKKulunTuo\nNH9zd6L2PUzz/dLXo3OIIAABCEAAAhCAAAQgMCUE/P3bXRo2aQPnYZRJv39brOGTpy6dHbxBQyhX\na/hkzTSGT07JA6zgi+Y0c2JzsvQ+6WEZsQciVp9WbBN3jdK8BMEL0rt9TKbtUaVdo83HJH8JeoGN\nnI8pnC9dJXlpAk98wuQnIRYCBCAAAQhAAAIQgMBkEtip9d5uk3mz7t66O+gdTDKAMggOnzND5s0G\nbnbwyuYGZp+czIfGtQ4gUOWxwMUc/M3cunXrirmK1A0CEIAABCAAAQhAoAQIbNSQybUyb2s37goe\n3pHs+7cafQDXqjXfbN48icnCxvoSuFOqWG4E1Fl2r3xba+Z9JemZyzyHfQhAAAIQgAAEIAABCBQ9\nAXdaPN2+R+YtZeCSrv/m799O1rdvr5eBO2lxczCzllfmon/YFVpBWmaFPnhuGwIQgAAEIAABCJQj\nAU9g8qAmMLGB8wyUW7q99HHusLixLnjDstTwSS8fwPdvuZmRY+oJYOam/hlQAwhAAAIQgAAEIACB\ncRDoGxwKv3uzgfM3cG0JF/B+1ezG4e/fVrU08v3bOJ4Bp04NAczc1HDnqhCAAAQgAAEIQAAC4yDQ\n1T8Q/GGzDdyu4A9b2oM9A0M5S/P6b8fObwrWqAdujYZQLtRslAQIlDIBzFwpPz3qDgEIQAACEIAA\nBCqIQDwDpQ3cPVs7gn4NqcwV6qurgtcuapZ5mxOcoglMWuq9fDIBAuVBADNXHs+Ru4AABCAAAQhA\nAAJlScDfvNm83awhlA9u7wxy27dAE5ZUh4t3r1k2JzhRRq5R+wQIlCMBzFw5PlXuCQIQgAAEIAAB\nCJQwgQ0de4ObX0wZuCfauhPdydzptcGp0fDJ47SUQG01C3gnAkemkiaAmSvpx0flIQABCEAAAhCA\nQOkT8BICT2kJgZSB2xWs7+hJdFPLZ9bLwM0Jv4E7au7MYFqVPoojQKCCCGDmKuhhc6sQgAAEIAAB\nCECgWAgMycB54e6boyGUSZcQOEwzUPr7Nxu4Q5obmIGyWB4o9ZgSApi5KcHORSEAAQhAAAIQgEDl\nERgYGgru3dYZGrhb9A2cJzTJFdzXdvS8WcFpyz0D5ZxgiXrjCBCAQIoAZo6WAAEIQAACEIAABCAw\nYQR6tGSA14DzEMrbNrcFHX2DOa9VreGSrQtmBWuWzwlO1RIC8xrqcp5DBghUIgHMXCU+de4ZAhCA\nAAQgAAEITCCB7v7B4Peb28MeOK8BtzfBGnCpJQRagtM0fPKUJbOD5npeUyfwEVF0mRDgX0mZPEhu\nAwIQgAAEIAABCEwlgfbe/uC2TSkDd9dLuxOtATejZlpwsoybh1CetKiFJQSm8gFy7ZIkgJkrycdG\npSEAAQhAAAIQgMDUE9i+ty/89u0mDaG8f3tHMJhgEbjmuprgDRo6aQN3/MLmoJ4lBKb+QVKDkiWA\nmSvZR0fFIQABCEAAAhCAwOQT2NTVEy7g7W/gHt7ZlagC8xtqwwW8PYRy9fymoGYaSwgkAkcmCOQg\ngJnLAYjDEIAABCAAAQhAoJIJeA2457SI99oXZeD0DZzXg0sSls6oV+9bysAdyRpwSZCRBwJjJoCZ\nGzMyToAABCAAAQhAAALlTcAG7vG27mED93xnskW8X6F1306LFvFe1dLIGnDl3Uy4uyIggJkrgodA\nFSAAAQhAAAIQgMBUExgc2hc8uKMzWKshlGvVA/fSnr5EVTpizoywB26NvoM7qKkh0TlkggAECkMA\nM1cYjpQCAQhAAAIQgAAESo5A76DWgNPMkzZwXgOuvXcg5z34c7fV4SLeqTXgFmk4JQECEJgaApi5\nqeHOVSEAAQhAAAIQgMCUEOjqHwj+sNkGLrUG3J4Ea8B5wpLjFzaFQyg9E+Wc6bVTUncuCgEI7E8g\np5mrqqr6tk55h7RN46eP8ulKu0zRX0jbo+I+rWPXRccuVfwhaVD6qNKvj9KPU3yV5P53571IxxJM\nYOuzCRCAAAQgAAEIQAAC+RLY1dMf3LopNXzynq0didaA85IBJy1uDg3cKUtagllaUoAAAQgUF4Ek\n/yqvUpW/IX03o+pflRf7UnqaTN4R2j9HOlJaIt2otEOVz8buCuk86U7JZu4M6TcSAQIQgAAEIAAB\nCECgwAQ2d/UGazft0hICbcFD+hYuyV/QZ9VWh8bNywicKCPXUFNd4FpRHAQgUEgCOc2cjNitMmQr\nE170TOW7Wuf0Kl6v855RfILiDYqblH6Hy9G+jeFZEmbOQAgQgAAEIAABCEBgnAQ84OnZ3VpCQMMn\n/Q1c0iUE5mnI5Kla/80G7rgFs7QG3LRx1oTTIQCBySKQ08yNUpELZcrer+PrpI/rB6RN8VLJPW9x\n2Bil9Sv2dmZ6WtLLmyrXPXhWsGLFiqx5SIQABCAAAQhAAAKVTmBAM1A+rF632za3hyZuo3rjkoTl\nM+tl4DQDpUzcUawBlwQZeSBQlATyNXMeMvlZyT32jr8sfVDS/EYHBOcZKf2AzE6QMbxSkRW0trYm\nGRWQtRwSIQABCEAAAhCAQLkR6O4fDO7SDJT+Bu73MnG7+3LPQGkGh81u1PIBKQN3iNaD0x/Pyw0N\n9wOBiiOQl5mT2doak9IPwb9q+1fRvnvflqdRXKbtzZLTvR2HOD0tiU0IQAACEIAABCAAgWwEtu7p\nDW7b1B4uH7Au4QQmXkLgGC0h4N63U2Xilqg3jgABCJQXgbzMnAzcYhm6LRGKdyp+JNq+VvEPdfwr\nij0ByirpbuUdVFqndKL275I8PPPr0TlEEIAABCAAAQhAAAJpBPz929Pte4Jb1Pt2m/RE255EfGrl\n4E5Y1Bwu4O0lBGazhEAibmSCQKkSyGnmZMB+pJtbI83TtnvYPuN9ba9W7CGQG6T/IXl45KNKv0ab\nj0nu87/ARs7HFM6XrpK8NIEnPmHykxALAQIQgAAEIAABCARBnxbwvm9bR/j9m4dQbt3TlwhLU111\ncPLiluD1Mm+egXJmbc7Xu0TlkgkCECh+AlX+y08xB38zt26d51ghQAACEIAABCAAgfIisLtXC3hv\nSZm3OxV3J1jA2wSWacike96sozWU0ot6EyAAgfIloA6ze+XbWjPvkD/dZBJhHwIQgAAEIAABCEwg\ngS5NWHKzlg747fM7wp64wQR/V7dV86yTsYFb2TSdCUwm8BlRNARKhQBmrlSeFPWEAAQgAAEIQKBk\nCfRrCOUdmoHyNxt2BLdrEpPeBA6uvnpa8Fp9//aGpS3ByUtmB3P5/q1knz8Vh8BEEcDMTRRZyoUA\nBCAAAQhAoKIJ+FOWh3Z0Bb9RD9yNL+wKOhIsITBHhu31S1rCHrjjFzYH02tYwLuiGxE3D4EcBDBz\nOQBxGAIQgAAEIAABCIyFwIaOvWEP3PXP7ww2d+dexNtrvsXDJ4+YMyOYxvpvY8FNXghUNAHMXEU/\nfm4eAhCAAAQgAIFCENixty/43Qs7g99u2KllBLpzFrl4Rn1wxkFzpXnBwTJzBAhAAAL5EMDM5UON\ncyAAAQhAAAIQqHgC3f2DwdqNuzSRyc7gnq27g6EcE5k01dUEb14xJ3ibDNzR82YygUnFtyAAQGD8\nBDBz42dICRCAAAQgAAEIVAiBgaGh4K6XOsJhlF7Qu1cTm4wW6qurglM0ecnbVs4LTtJkJrWa1IQA\nAQhAoFAEMHOFIkk5EIAABCAAAQiUJYEhTWTywPbOcBjlTS/uCtq1NtxowcsItC5sCodQnrZsdjBT\nPXIECEAAAhNBgF+XiaBKmRCAAAQgAAEIlDQBz0T5uL59u0FDKG/QTJTb9E1crnBoS2PYA/eWFXOD\nBY11ubJzHAIQgMC4CWDmxo2QAiAAAQhAAAIQKBcC63fvDXvgrBc7e3Le1iKZNvfAvXXl3OAVzY05\n85MBAhCAQCEJYOYKSZOyIAABCEAAAhAoOQKbu3rV+5YycE+378lZ/6a66uCNyzSRiXrhjpk/i6UE\nchIjAwQgMFEEMHMTRZZyIQABCEAAAhAoWgI7e/q1kLcMnIZRPryzK2c9G7R4t9eCO11DKE9kIpOc\nvMgAAQhMDgHM3ORw5ioQgAAEIAABCEwxgY6+geBmTWDiXrh12zpyLiVQO60qOGlxS/BWrQd3ypKW\noKGmeorvgMtDAAIQ2J8AZo4WAQEIQAACEIBA2RLYo7Xgbt/crm/gdgR3bNkd9OdYDE7+LTh+QVPw\nFhm40zSUchYzUZZt2+DGIFAOBDBz5fAUuQcIQAACEIAABIYJxAbuxhd3ysC1ay24HKt560wv4u0h\nlG+S5k6vhSYEIACBkiCAmSuJx0QlIQABCEAAAhAYjUA+Bm6VlhI4XT1wb1k+N1gys3604jkGAQhA\noCgJYOaK8rFQKQhAAAIQgAAEchHIx8Atl2k7XUsJuBfu4OaGXJfgOAQgAIGiJoCZK+rHQ+UgAAEI\nQAACEEgnkI+BW6y14Dx88s0r5gSHz54RVFXpwzgCBCAAgTIggJkrg4fILUAAAhCAAATKmcC4DNxy\nGbg5GLhybh/cGwQqmQBmrpKfPvcOAQhAAAIQKFICGLgifTBUCwIQKCoCOc2chiJ8WzV+h7Rt3759\nR7n2Spuj6MfSSmmDdLaOtUXHLlX8IWlQ+qjSr4/Sj1N8leQB6tdJF+lY7umlfDIBAhCAAAQgAIGy\nJ9De2x/cuqktWLuxLbj7pd1BX45lBAxkeAglPXBl3z64QQhA4EACOc2cTrlK+ob03bTTP6Xt/5IX\nu1zGztvWJdo+QvE50pHSEulGpR2qfDZ2V0jnSXdKNnNnSL+RCBCAAAQgAAEIVCiBl7p7Q/O2duOu\n4IEdnTkX8jYmDFyFNhZuGwIQOIBATjMnI3arDNnKjDPP1P6aKO07itdKl0hOv1rn9Cper/OeUXyC\n4g2Km5R+h2L37NkYniVh5gyEAAEIQAACEKgQAh6Us75jb3CzDNwtMnBPtO1JdOcYuESYyAQBCFQY\ngZxmbgQeC/VjvMXHHMucLYjyLVXsnrc4bNSG0/olb2empyWxCQEIQAACEIBAORIYkoF7bFd32Pvm\nXrgXOnsS3eaSGfXBGzV88s0MoUzEi0wQgEDlEcjXzI1EKttcv/4ubqT0rOXIHHo4phWsWLEiax4S\nIQABCEAAAhAoXgIDQ0PBfds6QwN3i76D277Xf9fNHV6phbzXLJ0drFk2O/Ci3nonyH0SOSAAAQhU\nKIF8zdxW/bgujnrlFovdtoife9+Wp7Fcpu3NktO9HYc4PS3p5U2Ve6X2rKC1tZVJUrJSIhECEIAA\nBCBQXAR6BgaDOzRxiXvffr+5Lejo8yfzowdbtaPnzZR5mxOcKhO3bNb00U/gKAQgAAEIDBPI18xd\nqxI+IF0exb+ISnT6D2X0vqLYE6Csku6WORtUWqd0ovbvkt4vfT06hwgCEIAABCAAgRIlsHVPb3D7\n5vZQ67buDnoHc/8NtmZaVdC6oCnsfXuDDNy8hroSvXuqDQEIQGBqCeQ0czJgP1IV10jztO0ets9I\nNnHXaN9LELwgvVvy93OPKu0abT4mDUgX2Mj5mML50lWSlybwxCdMfhJiIUAAAhCAAARKh4C/f3tc\n37/dpqGTNnBPtSebwKShZlpw0uKWcAjlKUtagpl1OV9BSgcKNYUABCAwRQSqPKtUMQcPs1y3bl0x\nV5G6QQACEIAABMqaQHf/YHC3et1u39QeDp/c1eu/1+YOzTJs7nlzD9zxC5uD6TJ0BAhAAAIQGDsB\ndZjdK9/WmnkmfxbLJMI+BCAAAQhAAALB5q7e4DYZN/e+3betI+hPsIC3sS1qrAtOlXlbs3ROcMz8\nWYGHVBIgAAEIQGBiCGDmJoYrpUIAAhCAAARKisCgzNojO7uGDdxzu/cmqr+t2qs1gYmHTp6yZHbw\niuYGZqBMRI5MEIAABMZPADM3foaUAAEIQAACEChJAu29/cHdL3WEQyf/sGV3sLsv2fDJGRoueaK+\nf7OBe53i2dNrS/L+qTQEIACBUieAmSv1J0j9IQABCEAAAgkJDKj37VH1vt2p5QPu3NIeLuSd9Mv5\nZTPrw543G7hjNXyytprv3xJiJxsEIACBCSOAmZswtBQMAQhAAAIQmHoCW7p7Zdxk3l5qD+7Z2hF0\naTKTJKFa4yePmTcrOCWaffIgrf+mD/CTnEoeCEAAAhCYJAKYuUkCzWUgAAEIQAACk0HAC3fft60z\n1fsmA7ehoyfxZZvqqsPlA9z75riJ5QMSsyMjBCAAgakggJmbCupcEwIQgAAEIFAgAl5iyJOVeOjk\nHRo6+cD2zqAv4cyTrsKqlsbgxEXNwckycEerJ47ZJwv0YCgGAhCAwCQQwMxNAmQuAQEIQAACECgk\nAU9c4iGTHj55l3rftu3tT1x8S31N8FqZNxs4x/Ma6hKfS0YIQAACECguApi54noe1AYCEIAABCBw\nAIGu/oHgfg2dvFfrva2TiXu6fU/iiUuq9Z2blw6wefPQycNmNwbT+PbtAMYkQAACEChFApi5Unxq\n1BkCEIAABMqagL97e3BHl4zb7tDAPa5ZJweTTjspMktm1GvpgFTvW+vCpmBmLf+7L+sGw81BAAIV\nS4Bf94p99Nw4BCAAAQgUC4G+waFwwW73utm8PaxtLyOQNEzXMgHHybTFvW/LtYwAM08mpUc+CEAA\nAqVLADNXus+OmkMAAhCAQIkSsFF7oq077HmzgXMvXK8MXdLgBQI8cUn47Zt64LyEQB3rviXFRz4I\nQAACZUMAM1c2j5IbgQAEIACBYiVg8/aMvnMLv3mTHpC6B5KbN9/XwU3Tg9aFzUHrgqbg2AWzgpb6\n2mK9XeoFAQhAAAKTRAAzN0mguQwEIAABCFQOAX/z5mGTD27v0lIBqWGTe8Zo3pZpqORxMm7+5s0x\ns05WTvvhTiEAAQgkJYCZS0qKfBCAAAQgAIERCLT19GuoZKfMW2e4ztsTbXs0YUnyb95c7IKGWn33\n1hwcL+Pm798WaxITAgQgAAEIQGA0Api50ehwDAIQgAAEIJBBwIt0b+rqDR6QebNxs4F7vrNnzJxm\na723uOfNwyeZtGTMCDkBAhCAQMUTwMxVfBMAAAQgAAEIjEYg/t7Nxs0GzuZtp3rixhq8WLcnKvGw\nSX/3dkhzAzNOjhUi+SEAAQhAYD8CmDkaBAQgAAEIQCCNwI69fcFjWtfN37w9Gmms37u5OPe0HTN/\nVmjgViteMWs65o2WBgEIQAACBSWAmSsoTgqDAAQgAIFSIuCJSrwg96NWZNxe2tM35luYprUCDm2Z\nEZq2Y+bPDA0cE5aMGSMnQAACEIDAGAlg5sYIjOwQgAAEIFCaBAa1PMCGjr0ybu5xS/W8PbfbE5WM\n/X68SPdRc2eG5s06UtszaqvHXhBnQAACEIAABMZBYFxmrqqqaoOu3SkNSgP6KLxVaXO0/WNppeTj\nZyu9TbGHl1yq6EOS839U6dc7nQABCEAAAhAoNIHtGi7poZKPSY/IvD0hEzfWtd3iOnmyklSvm8yb\net0Ond0Y1EybVugqUx4EIAABCEBgTATGZeaiK50mU7Yj7aqf0vZ/Ke1ymTdvW5do+wjF50hHSkuk\nG5V2qPLZ2BEgAAEIQAACeRHw7JI7NCHJk23dwZO79gRPtnfLwHUH22Tm8gk1GjN5WEtjcIR629z7\n5l43ZprMhyTnQAACEIDARBMohJnLrOOZSlgTJX5H8VrpEsnpV+t/ur2K18vIPaP4BOkOiQABCEAA\nAhDIScDGzcsCPCHj9pTWcrNxe1Lfu+3qHch57kgZbNRs2GIdKiNXp2GUBAhAAAIQgECxExivmfOX\nBr+TMXP8//Q/2SsVL1S8xTfuWMcWRBCWKr4zDchGbTuNAAEIQAACEDiAgJcEeF7fuNm4PWnjZgPX\nvifo7s9/QEdzXY1M24zQuLnX7fA5M4KW+toDrk0CBCAAAQhAoBQIjNfMnSzDtjkybDcofmKUm9Zc\nXweErJ+dq5zzlNMKVqxYccBJJEAAAhCAQHkR6BkYCp7VZCThUMnIuHm/N5/ZSSI0tR4uOdvGTZqT\nMm9L1Qun/8eUFzzuBgIQgAAEKpbAuMycjZzJKd6m/zn+XJseNrlV24ujXrnF2t8W0XVP3PI00su0\nHZ6fGaIePvfyBa2trVkNX+Y57EMAAhCAQPETcG/bxq6e4Bn1sD23e29o4J5V7DQdyjvUV1cFqzQ8\n8lCZt8O0RMBhcxqDVc2NQS3DJfNmyokQgAAEIFD8BPI2czJsM3R702S8OqPt07X/d9K10geky6P4\nFxEGp/9Qeb+i2BOgrJLujo4RQQACEIBAGRHQ/xsCr9cWm7VnZd5s2rw0QP94XJsYzdQSAIdpNkkb\nt1dJnlnyoFkNml2SHrcyakLcCgQgAAEIJCCQt5lT2Quln0fDVVzOD/U/799q/x5tX6PYSxC8IL3b\n9dCxR5V2jTYfk/yl+gVKy//DBxdKgAAEIACBKSewSzNJ2rSFPW0ybc8oXq/9fJcBSL+hudNrQ+Pm\n4ZIpNQZLZjBUcsofOhWAAAQgAIGiIJC3mZMRe053cEzmXSh9p9LelJnufR37vCKLAAEIQAACJUTA\nC26/tKdXE5L0BM937pUUq5dtvYzbeGaSTEewVCYt7nGLjdu8hroSokRVIQABCEAAApNLIG8zN7nV\n5GoQgAAEIDAZBLr6B4IXZNg8HDI0bJFpe1Fx3ziHR8b1d2/bK5sbgkP0jdsrFL9C37Yd3NQQNGr4\nJAECEIAABCAAgeQEMHPJWZETAhCAQFkQSO9l2+BeNpm3FxRvULxTQyYLFfxtW2jW0kzbIdpnKYBC\nEaYcCEAAAhCodAKYuUpvAdw/BCBQlgT6B4eCzd294QLbnilyo+JNaXGhetkMzzNJumftEPWw2by9\nUubNpm2BhkhG31WXJWNuCgIQgAAEIDDVBDBzU/0EuD4EIACBPAl09Q2EJs1mLTZtjm3atu7tG9dU\n/9mq1FJfo1kjpwcHybg5Xql4heJlM6cH1cwkmQ0ZaRCAAAQgAIEJJYCZm1C8FA4BCEAgfwIDQ0PB\ntj394cQjm7P0sO2WmSt0qNaC2stn1cukNcisybgpPkixTRvDIwtNm/IgAAEIQAAC4yOAmRsfP86G\nAAQgkDeBvQODwRYNhXypuy80bI63hLGkNdq2T0DvWlzZzF62uLdt6cx6rdc2Le974kQIQAACEIAA\nBCaPAGZu8lhzJQhAoIIIeNHs9t6ByJylmbXQqKWM20T0rKUjXtBQGyzVEEgbNMfLFHtIpPfpZaug\nxsitQgACEIBA2RLAzJXto+XGIACBiSLgyUW27+0Pe862SY63hz1p/al9be/o6Qt6B/dNVBXCcmv1\nnZoX0I4Nmk1avL1kxvRgeg09bBP6ACgcAhCAAAQgMMUEMHNT/AC4PAQgUDwE3JvW0TeYMmc2atFQ\nx3STZuPWph63yQpztCbb4sa6YHFk2paEhi1l2uZrtkgmHpmsJ8F1IAABCEAAAsVHADNXfM+EGkEA\nAgUm4G/Tdmn9NK+htlO9Z46H98PtvuH0Qk7Zn+s2PNnIwtCo1QWLGuuDRTJsiyLj5m0fq6+mdy0X\nR45DAAIQgAAEKpUAZq5Snzz3DYESJuAetD0DQ/omrT/sJWtLM2q7lLZTvWfphs15pyI0aJijTVpo\n1mTOFoeGLTZudcG86fSsTcVz4ZoQgAAEIACBciGAmSuXJ8l9QKCECQwOeXijTJmMmCcNscLtHm33\nybA5Hj6WyjOZPWiZaKuU4OGPHua4oDEVh9vSfPWmzdfEI96eUVvNotmZ8NiHAAQgAAEIQKBgBDBz\nBUNJQRCAgHvM9qoXzMbM8myN4Xbv4P77SkuZtpRRc56JnSok+bOZrmGNNmSeCdIGLWXYUnFs0ubq\nGNP3J2dKTghAAAIQgAAEJoYAZm5iuFIqBEqWgA1Zr2Zr7OwfDDplsroUd2lSkM7+Ae0Pphm0yKgN\nG7eUYRtQL1uxhRrN+jhXPWnuTXMcK9yXMZur4Y7xsUYNjazSt2wECEAAAhCAAAQgUOwEMHPF/oSo\nHwTGSGBgaCjo7rcGg25N/NEVGbLYmNmkOS2ObdbS02zYBmXoij3UV1eFa6XNrq8J49CghcYs3bSl\nTFpTHcMdi/15Uj8IQAACEIAABMZOADM3dmacAYGCE3Bvlmdc3GPJiDmOzVgYp2n4WJwWnvNy/ole\n26zgNx8VOEvfl7XIiKXMWcqgxUatRYZttgyb49DATa8JGmqqJ6oqlAsBCEAAAhCAAARKggBmriQe\nE5UsFgKpIYj7gp7BwfDbMBuwHsU2WKn91Ha4H5my0KBFefeGvWWp8+I0G7GpnMyj0Gw9lX6zesKa\n6mpSkgGLt5uH01LHXzZoNXyDVugHQXkQgAAEIAABCJQ9Acxc2T/iyrpBm61+9XL16Juv2Gj5+y8b\nrfQ0b9uEOc/e9O0w38vGrCc2aWGeVHrxD0Ac/zOv1Tdms2S83Fs204q2nRYastCgvWzY4rRZtTXB\ndH1zRoAABCAAAQhAAAIQmHgCmLmJZ8wV0gj4e67QREUGKmWqUibJpiub4YrzpI6l8thYuZz0tN7I\nmBXh/BuT2gbkw4IZGoLoafFj2WTNlPmyMYu390uLjZti52Gh6kl9ZFwMAhCAAAQgAAEI5EUAM5cX\ntvI+yWt+pb7finq3ZLI8FDA2TmFvVtyrlda7lZ423KMV5Y3PLYWJNabi6XruRM+i2Cgj5W/BvD1D\nBiw0Y+H2/uasMcOspZs3T63PbIxT8RS5JgQgAAEIQAACEJhcApi5yeU9oVdzz5ZnJownywgnxZCZ\nGp48I31bxzIn0oj3/S0XYWQCHoLYIIM1vdrGa1povhzbYMX7jbWp/TgtjLOk2bzZuLknDAM2MnOO\nQAACEIAABCAAAQgcSGDSzZxeWM9QNb4meSq6f9M3TpcfWK3KTvFQxA6v59X78qLL3g4XYY7SvBjz\n8PEozWaOEGgijSoZLZssGy4ZJRspxf6WK5WeGkbo/dB8yZQNbytvuB2enzJn02OTFpZRHZZPgAAE\nIAABCEAAAhCAwFQTmFQzJyNnA/cv0lukjdI9SrtWhu6xqQYx0df31PM7e/qC7Xv7g217HPcFWxXv\nUNxmMxabNZk096SVa7APShkrmaY0g2WT9LLZSpmuMG2/PC/3hrknKzRrGUbNZdRMYwKOcm0/3BcE\nIAABCEAAAhCAwMsEJtXM6bInSM/IvD3nKsjIXa3oTKmkzZx7xLbLmG2TMbNRi2Mbtti47ejpD0pp\nYo70YYM2TB4KGJswb8e9VR4+mN6rld7TFQ8xTB1P9Y65V4vhhPwEQQACEIAABCAAAQhAYPwEJtvM\nLVWVX0yrtnvnXpt5G3rZP09pVrBixYrMw1O2/1Rbd3DrprbIoPUHW2XWbOI85LEYQnWVp5P3hBkv\nT5bhb7L2m9kw7Zgn1UhNtJE2uYZ7uqRqhhIWwyOlDhCAAAQgAAEIQAACEBiRwGSbuWwfGx2wbJd6\n7q5Uja2gtbX1gOMj3s0EH3imfU9w5SObJvgq6rHUFVLreVUHzVrPq7muNlzTq7m+NlrjS9tK8zEv\nxuw1vrxtY0av14Q/Hi4AAQhAAAIQgAAEIACBoiAw2WbOPXHL0+58mbY3FwWJBJVY0FiXINfIWVpk\nuBY01AXzVY7j1HZtMGd6yqTZrNm0ef0vesZG5sgRCEAAAhCAAAQgAAEIQEAT/00yhHt0vVXqPTpY\nsbu4zpHeM8l1yPty82XAsoVqdaXNnS5zFpq0WsX1wfwwjkyb4nk6l4WYs9EjDQIQgAAEIAABCEAA\nAhDIh8CkmjkNnxyQkbtQFb1e8syW31bao/lUfCrOWShT9v7DF8uopYzbwqiXbY561OhJm4onwjUh\nAAEIQAACEIAABCBQuQQm1cwZs8zbdYqskguewfHCY4pnQpaSA0iFIQABCEAAAhCAAAQgAIGCEWBB\nroKhpCAIQAACEIAABCAAAQhAAAKTRwAzN3msuRIEIAABCEAAAhCAAAQgAIGCEcDMFQwlBUEAAhCA\nAAQgAAEIQAACEJg8Api5yWPNlSAAAQhAAAIQgAAEIAABCBSMQJUmJClYYRNRkGa/3K5yn5+IssdZ\n5jydv2OcZXA6BEYiQPsaiQzphSBA+yoERcoYiQDtayQypBeCAO2rEBQpYzQCxdrGDpJvm59Z8aI3\nc5kVLpZ9mcx1AtpaLPWhHuVFgPZVXs+z2O6G9lVsT6S86kP7Kq/nWWx3Q/sqtidSfvUptTbGMMvy\na4PcEQQgAAEIQAACEIAABCBQAQQwcxXwkLlFCEAAAhCAAAQgAAEIQKD8CGDm8n+mV+Z/KmdCICcB\n2ldORGQYBwHa1zjgcWpOArSvnIjIMA4CtK9xwOPURARKqo3xzVyiZ0omCEAAAhCAAAQgAAEIQAAC\nxUWAnrnieh7UBgIQgAAEIAABCEAAAhCAQCICmLkIk2auWS7dLD0uPSpd5EOK50g3SE9H8eyYrPYv\nlZ6RnpTempb+p9p/WHpI+q3kKU4JFUxAbWBM7Uv550puj13SN9LRaf84ye3Lbe+fpaoKRsuti4Ca\nQEHal8pplH4tPSH5d/ByAEOgUO0r43fsWpX7CHQhUMj2pbLqpCulpyT/jv0xhCFQ4DZWdO/4mLmX\n2/iANj+u5QYOV3yidIEe/hGKPyX9l9JXOY72/fLkY+dIR0pnSN9UWrVUo+2vSafpnKMVPyRdKBEq\nm8CY2pdQ9Uh/LX0iC7YrlHae5DZpuf0RKptAIdvXl/Tb9SrhPFY6Wb9pb6tstNy9CBSyffn/n+9S\nmV2QhUBEoJDt6y9V5jb9hh2q2O9pt0AZAiJQkDZWrO/4mLmojesf/hbpPu8q7lT0uLRUOlP6TpTN\n8VnRttOvVt5eab22n5FOkNxLYs3QQ3fcJG2WCBVMYKztS/m7pduFzKZuOKhJLdZOk47dIe3T9nel\ns9LzsF15BArVvlTOHulmE1Tcp8i/icsqjyh3nE6gUO3LZeo3bKai/y19DsoQMIFCti8V90Hpi1G5\nQyp7B5QhUMA2VpTv+Ji5LG1c/7NZqWT/VfouaaEbQfTD4HhBdIqN3ovRtqON0lLl7Vd8vvSwZBPn\nvwx9yxkIEDCBhO1rJFhud25rcQjb3UiZSa88AuNsX8PAVE6Ldv5I8ogEAgRCAgVoX59VMV+W9oAU\nApkExtO+ot8sF/lZbd8n/URamHkN9iubwHjaWLG+42PmMtq0HrL/avhT6WI9tI5RmrzdeWbYp/Nr\nlWgzZzO4RHpIujQzI/uVSWAM7WskQFnb3UiZSa8sAgVoXyEwlePh4j+S/lm/g89VFkXudiQC421f\nOn+1yn6l2tTPR7oG6ZVLYLztS+T8u+WRBL9XG3uN4jukL1UuUe48k8B421ixvuNj5tKedPSQbOR+\noB+Cn0WHtirdQ9v8guN4W5TuHpHlaaf7B8Q9caudpvOflTwM7hrpdWn52KxQAmNsXyNRcrtLH/YW\nt7uR8pNeIQQK1L5iWldq42n9hP1TheDjNnMQKFD7OkmX8QROGxTfLh2q7bU5Ls3hCiBQoPa1U6jc\n4xv/seAn2rapI0DA7/DubEn6jj8SsdU+UGzv+Ji56HHpIbvH41vS43pIX0l7itdq+wPRvuNfRNtO\nP0en1UsHa9sTUdwtbZKOUNr8KN9bFPv7O0IFE8ijfWWlpbbpob6dKu/EqMz3az9uk1nPIbH8CRSq\nfZmUyvqcombp4vInxx0mIVCo9qXfryukJdJKXfcU6Sltr0lSB/KUL4ECti//Af2XUtym3qTtx8qX\nHHeWlECh2piuV5Tv+CwaHrUEPWj/j+U2yd+6DUXJn1bs7+bcu7ZCekF6t/7ns8vHdY5nTfqgNCB5\nWOZvovT/qfgiyd/PPS+dq2P+ixGhQgnk2b42CFeTVCe1S6erHT2mslq1fZXUILnN/S+l+39ihAol\nUKj2JXwdkr8FfkLqjXB+Q83r3yoULbctAoVqX/79ioGqzJXa/pXSjgJyZRMoZPtSWQeJ5vekFmm7\n9OdqY353I1QwgQK3saJ7x8fMVXDj5tYhAAEIQAACEIAABCAAgdIlwDDL0n121BwCEIAABCAAAQhA\nAAIQqGACmLkKfvjcOgQgAAEIQAACEIAABCBQugQwc6X77Kg5BCAAAQhAAAIQgAAEIFDBBDBzFfzw\nuXUIQAACEIAABCAAAQhAoHQJYOZK99lRcwhAAAIQgAAEIAABCECggglg5ir44XPrEIAABCqZgKar\ndrhdelvMQdtnS7+tZC7cOwQgAAEIlA4BliYonWdFTSEAAQhAoMAEZNy8ztlPpGOlaukB6QytTfXs\nWC+lsqp13uBYzyM/BCAAAQhAIF8CmLl8yXEeBCAAAQiUBQGZsH/QjXRLM6LYCw+/WqqRLpNB+4Xy\nrNS2FyN2HocLlf4Hpa/R9mekLdJqpR0RHiVAAAIQgAAEJoEAZm4SIHMJCEAAAhAoXgIyZDZo90l9\n0q+kR2XKvq/0Fm3fLbnXbp80pPQepa/S9o+03RqZuV9r/yjtr1dMgAAEIAABCEwaAf/VkQABCEAA\nAhCoWAIyYd0yZT8WgC7pbOmPtP+JCMh0xSukzdI3lL5asYdSHhodd3Q3Ri6NBpsQgAAEIDBpBDBz\nk4aaC0EAAhCAQBETGFLdrCrpj2XOnkyvq0zcZdrfKh0jefKwnrTjHqJJgAAEIAABCEw6AWaznHTk\nXBACEIAABIqYwPWq2/+SebOpCxR5iKVDs7RFJs+G732SJ0shQAACEIAABKaUAGZuSvFzcQhAAAIQ\nKDICn1V9aqWHZOQeUex9h29KH1DanYo9xJLeuAgMEQQgAAEITB0BJkCZOvZcGQIQgAAEIAABCEAA\nAhCAQN4E6JnLGx0nQgACEIAABCAAAQhAAAIQmDoCmLmpY8+VIQABCEAAAhCAAAQgAAEI5E0AM5c3\nOk6EAAQgAAEIQAACEIAABCAwdQQwc1PHnitDAAIQgAAEIAABCEAAAhDImwBmLm90nAgBCEAAAhCA\nAAQgAAEIQGDqCGDmpo49V4YABCAAAQhAAAIQgAAEIJA3Acxc3ug4EQIQgAAEIAABCEAAAhCAwNQR\nwMxNHXuuDAEIQAACEIAABCAAAQhAIG8C/x+PkM4Mao066gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 1500x300 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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KCyqhoyCQbwGSuXyLsj4EEEAAAQQQQACBshSIrvlYCdzDuh/uSRff0pR1H0O7\n7uLqLzjX1ZxwrAuEw1n7MQOB/gqQzPVXkOURQAABBBBAAAEEylag8yXfdz/g2l+Zlf1+OAmEDz1I\nSdw5LjxlMg81Kdszorh2jGSuuI4HW4MAAggggAACCCBQBALxtnbX9uwLruXuB11kyYfZt0gPNak5\n7hjvcsrwXntm78ccBAZAgGRuAFBZJQIIIIAAAggggEBpCsQ++dS1PPCoF3G9Ky5bCYwc4erOPNXV\nnv0lF9p+VLZutCMwoAIkcwPKy8oRQAABBBBAAAEESkGgY7FeLaBRuLZnX3QuEsm6yaE9dnX1556l\n++G+4AI11Vn7MQOBQgiQzBVCme9AAAEEEEAAAQQQKDqBeCTq2l+e6Zp1P1xkwcLs26eXelcfNdXV\nfflMFz74AO6Hyy7FnAILkMwVGJyvQwABBBBAAAEEEBhcgdjmza714Sddy30Pudja9Vk3xns/3Gkn\nubpzTnehcWOz9mMGAoMlQDI3WPJ8LwIIIIAAAggggEBBBToWvedadS9cq11K2daW9btD48fpBd9n\nuJpTv+iC9bwfLisUMwZdgGRu0A8BG4AAAggggAACCCAwUALx1lbX+syLXhIX0X1x3ZXw5IO9p1JW\nH97oAsFgd12Zh0BRCJDMFcVhYCMQQAABBBBAAAEE8ikQWbHStT6oUbjHnu72Bd+uutrVnny8RuLO\ndFV62TcFgVISIJkrpaPFtiKAAAIIIIAAAghkFYjrKZT2Yu+WBx5xHXPnZ+1nM4I7jtarBU5ztWec\n4oIjhnfbl5kIFKsAyVyxHhm2CwEEEEAAAQQQQKBHAtH1G/RAkydc60OPO3tPXNZiT6WcMtl7N1z1\n4ZNdIBTK2pUZCJSCAMlcKRwlthEBBBBAAAEEEEBgG4F4LOY65s13Lfc/4tpfneVcNJZVKKCRt9ov\n6amUGoXjqZRZmZhRggIkcyV40NhkBBBAAAEEEECgUgVim/RaAd0HZ/fDRVeu7pahav99XJ1G4Wq+\ncLQLVIe77ctMBEpRgGSuFI8a24wAAggggAACCFSQQDwed5FFi3Uv3GOuzV4r0N6efe/ral3tiXqg\nydmnuardd83ejzkIlIEAyVwZHER2AQEEEEAAAQQQKEeB2OcbXeuTz7rWR59y0aXLu93FkJ5EWXfW\naa7mpONdUC/7piBQCQIkc5VwlNlHBBBAAAEEEECgRATi0ajrmPOGa3nkSe/JlE5PqMxaqqp0CeVR\nrlZJXPjA/VxADzihIFBJAiRzlXS02VcEEEAAAQQQQKBIBaJr1moE7knX+vgzLrZufbdbGdxpjB5m\ncqqrPe1EFxy1Xbd9mYlAOQv0OJnTv3TYs1vnKFbpuuXT9XmUpu9UNCiWKc5X+2eq7V9FfqzqckVU\ncbXan/DbJ6uerqhTPKq4RvPiNo+CAAIIIIAAAgggUFkC8bZ21/byTNeqUTh7MqXr7mdhKOiqpx7m\nak8/hdcKVNZpwt52I9DjZE7ruEaxSJF8q+J1mn5Gudj1St5s2uJaTe+r+gLFfopxiqfVNkn9LLG7\nUXGF4jWFJXOnKB5TUBBAAAEEEEAAAQQqRCCy5EMvgWt96jkX37yl270OTRjnvVag5uQTXGh7G0ug\nIIBAUqBHyZySsQla4EuKf1P8vb/wWaqn+dM3q35eca3C2u9Q8tameqmWXaJ6iuplqoerfaZqG727\nRdXZCpI5A6EggAACCCCAAAJlLBDb0uTannnBS+Iii9/vfk9ralzNcUfrMsqTuBeueynmVrhAj5I5\nGf1G8SPFsBSvHZWYrbHPVis5G+PPG6/aRt6SZaUmrK1DYdPp7SlNTCKAAAIIIIAAAgiUi4DdTdMx\n/20vgWt74RXn2uzf+rOXqr0nJUbhjj/WBYcOyd6ROQgg4AnkTOaUpJ2ufuv0H+NcTU/rgVumxwjZ\nfXHZ2rusUt9jl2JauIkTJ3aZTwMCCCCAAAIIIIBA8QrYy7ztEkqL2Crv3/6zlsDwYa5WrxOwUbiq\n3Ruy9mMGAgh0FciZzGmRoxRnKsE6TXWtYrimb1W9VvVYf1RurD6v81dvo287+9NW2SWaqxXWbtPJ\nkmxPaUpMap03acrCNTY28oCULkI0IIAAAggggAACxSUQ27jJe6F365PPucjCd7vfOL1CINx4iJfA\n1Rw91QWqw933Zy4CCGQUyJnMKbGyJ1Na2H1u01T9UG1f0/R/aPoSxfV+/YD1UXlQcbvm/1q1PQBl\nT8VsLRNV22bFVH2epbhY8VsFBQEEEEAAAQQQQKAEBeLtHa595mzvxd7tr+mh5929E077F9xxtKs9\n9UQvQnq9AAUBBPonkDOZ62b1lsTNUHJmryBYoTjP+ippe0dtMzS5UBFRXGWJnM1TuVIxXWGvJrAH\nn/DwE4+FggACCCCAAAIIlIaAfte5yIKFXgLX9txLLq4Hm3Rbwnqx91FT9UqBk1340INcIGRvu6Ig\ngEA+BAL2H2QxF7vMcs4c/UsPBQEEEEAAAQQQQGDQBCIrV7m2J55N3AenF3znKuED93M1uheuZtrR\nLjhsaK7uzEcAgW4ENFg2V3lbY3qX/ozMpa+LzwgggAACCCCAAAJlJBD7fKM3+majcJGFi3PuWWjn\n8V4CV3viNBcau1PO/nRAAIH+CZDM9c+PpRFAAAEEEEAAgbISiLe1b3sfXDR5t0zm3QyMGO5q9CqB\n2pOPd/ZqAY0gZO5IKwII5F2AZC7vpKwQAQQQQAABBBAoLYF4hx5kMucNPY3yJdf+ymsu3tTc/Q7o\n6ZM1Rx7ujcJVHz7ZBar4Sdk9GHMRGBgB/ssbGFfWigACCCCAAAIIFLVAXE+e7Jg330vg2l56NfeD\nTLQ34YP2T9wH94WjuA+uqI8uG1cpAiRzlXKk2U8EEEAAAQQQqHiBuC6Z7Jj/tvc+uLYXlcDp3XC5\nSmjiBCVwx7naLx6n++B2zNWd+QggUEABkrkCYvNVCCCAAAIIIIBAoQXisZjr0KsE7EEmbS+87OKf\nfp5zEwKjRrraacd4o3BVe+/JfXA5xeiAwOAIkMwNjjvfigACCCCAAAIIDJiA9y64d95NJHDPv+xi\nGz7J+V3eg0x0+WTNccd4l1PyPricZHRAYNAFSOYG/RCwAQgggAACCCCAQP8FvARu8RIlcLqEUqNw\nsbXrc640MHSIqzn2SO9plOFDDuRBJjnF6IBAcQmQzBXX8WBrEEAAAQQQQACBHgvYJZSRd9/TA0xm\nujYbgVv9cc5lA/V1rvroI5TAHeOqGw9xgXA45zJ0QACB4hQgmSvO48JWIYAAAggggAACGQW8p1C+\nucBL4Npffq1Hl1C6utrEqwQsgTtMrxKoqc64bhoRQKC0BEjmSut4sbUIIIAAAgggUIEC8ZZW1/76\nXCVwr3kv9I5v3pJboabG1RxxmHcPXPXURheorc29DD0QQKCkBEjmSupwsbEIIIAAAgggUCkCsU2b\nXfursxIjcK+/4VxbW+5d18u8q6c0epdQ1hwxxdkllRQEEChfAZK58j227BkCCCCAAAIIlJhAdN0G\nXTqp+990CWXH/AXORWO590CXUFZPmexqjjnSVR85xQWH1Odehh4IIFAWAiRzZXEY2QkEEEAAAQQQ\nKFWByPKPXLs9wERhDzPpSfFeI3Dk4a76mCP0EJODdQ9cTU8Wow8CCJSZAMlcmR1QdgcBBBBAAAEE\nilsgHom6jncWufbXXvceYBJdsbJHGxzccXRi9E0JXHj/ffUagVCPlqMTAgiUrwDJXPkeW/YMAQQQ\nQAABBIpEIPbpZ6599lwlcHP0IJN5Lr6lqUdbFtp1FyVwGn1TVO25uwsEAj1ajk4IIFAZAiRzlXGc\n2UsEEEAAAQQQKKBAPBrVC7zfTyRvSuBsuqelat+9EwncsUrgJozv6WL0QwCBChQgmavAg84uI4AA\nAggggED+BbynT2rUzRt9mzXHxTdu6tmXhEIufMiBiQTu6KkutMP2PVuOXgggUPECJHMVfwoAgAAC\nCCCAAAJ9EYjH4y665EPX5idvkXfedS7Wg6dP6svsASb2BMrqwxv1DrjJLjhsWF82gWUQQKDCBUjm\nKvwEYPcRQAABBBBAoOcCsaZm1zH3zcTDS2bNdbENn/R44aq99vRe3m1h0wGNyFEQQACB/giQzPVH\nj2URQAABBBBAoKwF4u0d3pMnO+bNd+1K4rxXB/Tk3W9SCQwd4qoPOzSRwGkULjhqu7K2YucQQKDw\nAiRzhTfnGxFAAAEEEECgSAXiukwyoksnO+YmkreOt95xrq2tx1sb2q3BH307zIX321uvD+CnVo/x\n6IgAAr0W4C9Mr8lYAAEEEEAAAQTKRcDue4utWuONull0vPGWi+tBJj0udbWuevLB/r1vjS40ZnSP\nF6UjAggg0F8Bkrn+CrI8AggggAACCJSUQOyTT137vLcS977Ne9PF1q7v1faHdtk58fAS3fsWPnB/\nF6gO92p5OiOAAAL5EiCZy5ck60EAAQQQQACBohSI6QXdHfPf7rzvLbp0ea+2Mzh6exe20bdDD1Z9\nEK8O6JUenRFAYCAFSOYGUpd1I4AAAggggEDBBWzkze51S0bkw2U9fmWAbaw9uMTe+2aXT1oSF9p5\nvAsEAgXfD74QAQQQyCWQM5nTH6+dtZJbFDsp7OUpN+n68hvUPkrTdyoaFMsU56v9M9X2B+/Hqi5X\nRBVXq/0Jv32y6umKOsWjims0L27zKAgggAACCCCAQG8Fkve8pSZv0VWre7caXSYZPmDfRPKm0beq\nSbvz2oDeCdIbAQQGSSBnMqftiih+oD+W85Sk2Rst56p+SvWlimfUfr0+X6dpi2s1va/qCxT7KcYp\nnlbbJPWzxO5GxRWK1xSWzJ2ieExBQQABBBBAAAEEcgrEo1Fnl0l6yZtdOrlgobORuF6VYFAJ2x6J\nSyd12WR4/31coKamV6ugMwIIIFAMAjmTOSVha7ShFk7Tm5WYLdLkeMVZimn+Ttys+nnFtX77Hepr\nz/Fdqv5LVE9RvUz1cLXPVG2jdzbad7aCZM5AKAgggAACCCDQRcDe82bvdvOStwWKtxe5uO6B622x\nh5aEDz1I970peTvkABccZv8+TUEAAQRKWyBnMpe6e0rAGvT5EMUsxY5+omdJ3hrNG+P3tUTPRt6S\nZaUmrK1DYdPp7SlNTCKAAAIIIIBAJQtENcoWWbjYdSxa7CJK3Kx2Suh6VUIaedtTI28H7pcIXUIZ\nHDmiV6ugMwIIIFAKAj1O5pSsDdUO3aP4npK3Tfqcbf8yzbD74rK1d1mP1m2XYlq4iRMndplPAwII\nIIAAAgiUvkC8tdV1LF7iIkrYOpTARRa952LreveaAE9Bl0iG991ra/K2r17WXW+351MQQACB8hbo\nUTKn5MpeoGKJ3G1K5O71Sdaqfaw/KjdWbev8dht9s4emJMsETdidyNZu0+ntKU2JSa3zJk1ZuMbG\nRh6Q0kWIBgQQQAABBEpLIB6Luejyj/ykLZG4RZYu06PS7NlqvSuB4cO8B5YkR96q9tQDS8K86613\nivRGAIFyEMiZzClhsxG1PyoWKcn6dcpOP6jpSxTX+/UD/jxrv12LWV97AMqeitlaNqo2u+duqj7b\nZZoXK37rL0OFAAIIIIAAAmUk4L0ewBtxe88bebP73uLNLX3aw+CY0VtH3XTppN3/FtBDTCgIIIBA\npQvkTOYEdJTi64oFSsTe9MF+otqSuBlqs1cQrFCcZ/OUtL2jthmaXKiwJ2FeZYmczVO5UjFdYdc+\n2INPePiJx0JBAAEEEECgdAViGze5yPsfuMh7isXve0lcbG0fLpc0gnCV7nfb3YX3nuSq7NLJA5S8\n7ZS8Lb90jdhyBBBAYCAEAkq0BmK9eVunXWY5Z86cvK2PFSGAAAIIIIBA3wTsN4Pd0+Ylbu9/2Fn3\n6T43fxNC48cpaZvkwvvs5SVvVbvv5gJ67xsFAQQQQGCrgAbL5upvcGO6SU9G5tKX4TMCCCCAAAII\nlLmA9z63lau3TdyWfOjiGoXra/HudUsmbRp5C+8zyQVHDO/r6lgOAQQQqHgBkrmKPwUAQAABBBCo\ndAHvXW7Llm9N3N7TEyY/XOZcS2vfadIvl1QSFxw/1t4z2/d1siQCCCCAwDYCJHOcEAgggAACCFSI\ngD1RMrZmbSJx+3C5iy5VAqeIrtADpyN2m3sfS5Xuc9t1F93rtpt3v1uVRty4XLKPliyGAAII9EKA\nZK4XWHRFAAEEEECgFAS8e9vWf6JkbZmXrEWWrkgkbsv1vLLWtv7tQl2tq9pDSZuFJW4Wu07k1QD9\nU2VpBBBAoE8CJHN9YmMhBBBAAAEEikMg9vlG75LI5ChbZFkicYtvaer3BgZ0P1siYduauIUmjOO1\nAP2WZQUIIIBAfgRI5vLjyFoQQAABBBAYMAHv8kg96j/60UoXWbFKDyZRKGmzUbe4krl8lOCOo7eO\ntPkjbsHR23OPWz5wWQcCCCAwQAIkcwMEy2oRQAABBBDorUBs8xYvYYt+tNpP3GzakrfVzrW393Z1\nGfvbEyWrdmtwIbvHrWFiotZ0UO0UBBBAAIHSEiCZK63jxdYigAACCJS4QFwPGonqIST20BEvcbOR\nNhtxU9IW/+zzvO1doK6uM1EL7ZZI3OwhJYFR2zHaljdlVoQAAggMrgDJ3OD68+0IIIAAAmUoEG9r\nSyRsq9ao/tjFVn/soqs1rRG2qKad3uGWt6IXbFftsnWELZG4aaRNl03yGoC8KbMiBBBAoCgFSOaK\n8rCwUQgggAACxSxgT4uMf/qZl6hZcpYaMSVtsU8+zfvm2+WRoZ3HKya4qomJ2hK30NidXCAUyvv3\nsUIEEEAAgeIXIJkr/mPEFiKAAAIIDIJAXC/Mjq5d56Ifr/VH1vzRNUvelMT1+xH/mfZJ72sL6cXa\nlrCFlLBVWe0lcONdcOSITEvQhgACCCBQwQIkcxV88Nl1BBBAoFIFvKdD6v60mJesrXexdesST4tc\np2l7aqTa45s2DxhPcPtRiSRtopK1CUrW/MQtuNOOLlDFKNuAwbNiBBBAoMwESObK7ICyOwgggAAC\nzsVbNaq2bkMiWVNy5iVtKYlaTPOcHkQyYCUYdMExo11o3E7eZZCh8Tu54FiNuNlnG2UbUj9gX82K\nEUAAAQQqR4BkrnKONXuKAAIIlLxAXA8OsfeqRTd84mIbPlVYnYjUtoEcVUsi2tMig0rSQuP8JM1L\n2jSt2nv4SDhc8t7sAAIIIIBAcQuQzBX38WHrEEAAgYoQ8B4o0tS8TXJmyVoiQUsmbErePtWDRaKx\nwpjooSLBMTu40I5jEgmaJW6WsNnomhK4wIjhPC2yMEeCb0EAAQQQyCJAMpcFhmYEEEAAgf4JdCZo\ndm+anvxo71Dz7lPTtBednxPtTo/zL2QJDBvqjaCFdDlk0BI2xdbParP3sfGUyEIeEr4LAQQQQKCX\nAiRzvQSjOwIIIFDJAvGILnPctMnFNibCS9CSidmnlpRtm6S59vbB4QrpnrXRGlXzEjXFGCVrOylZ\ns/vYvM8K7lsbnGPDtyKAAAII5E2AZC5vlKwIAQQQKC0Bb+RsS5OLW2Km+9AsQfOmvdioe9MSdWp7\nfPOWQd9Jb0Rth+29CHn1KP+z1TskPm83klG1QT9SbAACCCCAwEALkMwNtDDrRwABBAZYwB6zb/eb\nxTdvdrFNW1RvcTFNe7Uer2/tiWmbZ30Ulqxt1KP39UCRoinV1RpNU4Kmx/Ynk7VEnficTNwCNTVF\ns8lsCAIIIIAAAoMpQDI3mPp8NwIIIOALxNvalZA1uZiNlKmOb1Fyts1nzbPEzBKxLUrKUpM2LeOU\n0BVlUeIV1ChZcJTCq7fbpg4k2+z+tPo6HihSlAeRjUIAAQQQKFYBkrliPTJsFwIIlISA96j85hYX\nb1FY3WxJmH22Wk9ntBEzS9D8JC1bsuY6BvCdZ/mUDARcYPgwF7QnOSqCI0ZsTdTSkrWAEjh7fH9A\ny1AQQAABBBBAIP8CJHP5N2WNCCBQxALxjg4lWq3eS6W95Ku1LZGI2WdrTyZlloSlTncmbIlkLWbJ\nmtqcli/pUlfrJWaWlCWSM02PtERNSZq1bzOtPkOHukBVqKR3mY1HAAEEEECgXARI5srlSLIfCJSB\ngD2Qw0UiiQTLHlNviZZqL+Hya3t8fXqbl5h5iVgiGfOWs0Qs2Z4yv6juEcvjMfNGwIYr0bKHgwwb\ntrVWW1BtAbV5tUbVvOmRlqxpmvvP8ngUWBUCCCCAAAKFFSCZK6w334ZAyQjYQzWcjWK1d3TWcXvM\nvMK7v8uSKpvXbsmV9VPypc9eu+Z39vP7e59Tp5PrsEQrJXEr2Auhi+1I6H1mgaFDvLBH5geGJKat\nDvrtXhKWnrBZcmbzw+Fi2yO2BwEEEEAAAQQGWIBkboCBWT0C3QkkR6KcvbsrosTIart3StOJWqNU\n9tmSKoU3amW12jrb1Zacv3WZZJ+tiVhnHy8h89fn116SlkzckvNsvZQeCXijYvbwDi+UiKVOW3Jm\nyZYlZ5ak6TLF4FA/WbPPNpJm7zvTCBn3lvWIm04IIIAAAggg4AuQzHEqFJ2Al+BENSpkI0MxJTdW\nd37WI9i9z3qcuhf63Dmtvl6/DO0py8Qj/vL2OHdLWPzlO6e9xCrR3rnulLZE4pU+X8mV+njJli2b\nkpx5iVfUEi5/nj0ww0vY/O8uuiNQ5hukl0l7yZfuFQvUKvzaJac1L5hMxizZ8hI1P0Hr/OwnbvbZ\nlgsGyxyN3UMAAQQQQACBYhQoeDKnf3k+RRA3KOwO+j/oh/v1xQiTaZs6Fr/vNl//G+eCepqb/XgL\nKDTtbFrh/au6P60P2kNrS+njT3vLesv7/dOf9Nblc/rWbH0yXPq/5HuJkFN4ldVpYavy27y+neHN\n6NI/HvPbLEHqnFYSZMtZgpRMvOKWROkFxKntXjLm990mGUskW4lkLZmcbU2wvHVSKlfA/ruxBMnu\n5arVaJWNWKXU3giWPz/RXp0Y1UomaJacdSZqSrpsWa/NpmudC1cxAla5Zxd7jgACCCCAQFkJFDSZ\nU+JhCdz/U5yoWKl4XW0PKgFYWAqq9uS66IfLSmFT2UYE8iNQHXYBvcjZ6X4suycroM9eMmXtlmx5\n8y3hqk7082v77LRcZ3vK9DbzLCnzE7VEkqZ1VRX0z1J+nFgLAggggAACCCAwCAKF/tU0Rfu4RMnb\nh7avSuTuUHWWoiSSuaJ9Ke8gnDh8ZR4F9OALpwQmoBEjZw/BsGTGS55UW7slUN581dZu8y2J8vr5\n7anzU5brXJetwxKq5Dq8JMxfX7Lda9uauDGClcdjzKoQQAABBBBAAIEBECh0Mjde+/BRyn6s1PTh\nA7BfA7NKu8yQUhiB5KWoVttldymfA5b8qM0SHy/58WvvUtf0dm/5ZB+7vDWx7NZ1KBmyd2Ylkygv\nsfITquT6LWmybfBqf34oMe0t67cnp71+XrslXKq9BCzRLzkvoHmJPlzyV5gTim9BAAEEEEAAAQTK\nT6DQydzWm722WnbJkDRid4VmW7iJEycWjXrVfnu57f70u8RDNvx7xBL3jfn3i/n3iHn3fKXfL5bS\nP3EfWuJ+sq6PYU/jSL9/LGV24v64VB6bqfv57J47k/ZquzfPn7aJzvZkP2vz2735CrsP0Kat1vIB\nv+56f2Bifmd7el/vvkElMV43P5HKkJxtTdbUNzk//b7BojkL2BAEEEAAAQQQQAABBIpDoNDJnI3E\n7Zyy6xM0vTqdQknKTWqzcI2NjV2SvfT+hfoc1BPtgrvvWqiv43sQQAABBBBAAAEEEEAAgawCGi4p\naHld37anRo52VejmHHeB4sGCbgFfhgACCCCAAAIIIIAAAgiUgUBBR+Y04hZREvcduT2h0DV17k9q\ne6cMHNkFBBBAAAEEEEAAAQQQQKCgAgVN5mzPlLw9qsqCggACCCCAAAIIIIAAAggg0EeBQl9m2cfN\nZDEEEEAAAQQQQAABBBBAAIFUgUDXJyIWF5Auy1yvLVpeXFvlbc0Oig1FuF1sUnkIcH6Vx3Es1r3g\n/CrWI1Me28X5VR7HsVj3gvOrWI9M+WxXsZ5juyhvG53OXPTJXPoGF8tnJZlzBNpYLNvDdpSXAOdX\neR3PYtsbzq9iOyLltT2cX+V1PIttbzi/iu2IlN/2lNo5xmWW5XcOskcIIIAAAggggAACCCBQAQIk\ncxVwkNlFBBBAAAEEEEAAAQQQKD8Bkrm+H1PvpeYUBAZIgPNrgGBZrSfA+cWJMJACnF8Dqcu6Ob84\nBwZaoKTOMe6ZG+jTgfUjgAACCCCAAAIIIIAAAgMgwMjcAKCySgQQQAABBBBAAAEEEEBgoAVI5nxh\nPblmZ8VzikWKdxTX2CzVoxRPKd736+2SB0Wff6xYolisODml/av6vEDxluJxhT3ilFLBAjoHenV+\nqf/2Cjsftyh+l0qnz5MVdn7ZufdfikAF07LrEtApkJfzS+upVzyieFdhfwevBxiBfJ1faX/HHtR6\n30YXgXyeX1pXteImxXsK+zt2LsII5PkcK7rf+CRzW8/xiCZ/oNcN7KN6quIqHfx9VV+neEbte1rt\nf7YfTzbvAsV+ilMUv1dbSFGl6RsUx2mZA1W/pfiOglLZAr06v0TVqvgnxQ8zsN2otisUdk5a2PlH\nqWyBfJ5fv9Tfrr3FeYjiKP1NO7Wyadl7CeTz/LL/f56jdW5BFgFfIJ/n1z9qnev0N2ySavud9gLK\nCEggL+dYsf7GJ5nzz3H9h79GMc8+qt6sapFivOIsxc1+N6vP9qet/Q71bVMs1fQSxRSFjZJYDNFB\nt3q4YrWCUsECvT2/1L9J8bLILKnrLDqlxurDcM2bqYhr+hbF2al9mK48gXydX1pPs+I5E1Tdrsr+\nJk6oPFH2OFUgX+eXrVN/w4aq+nvFv6KMgAnk8/zS6r6h+IW/3pjWvQFlBPJ4jhXlb3ySuQznuP5n\n06Bm+1fpWYod7STw/zBYPcZfxBK9j/xpq1Yqxqtvh+orFQsUlsTZvwz90TpQEDCBHp5f2bDsvLNz\nLVm88y5bZ9orT6Cf51cnmNYzUh/OUNgVCRQEPIE8nF8/02p+pWiGFIF0gf6cX/7fLFvlzzQ9T3GX\nYsf07+BzZQv05xwr1t/4JHNp57QOsv2r4T2K7+mgbermlLfsPL3EtXxYjZbMWTI4TvGW4sfpHflc\nmQK9OL+yAWU877J1pr2yBPJwfnlgWo9dLv5XxX/p7+CHlaXI3mYT6O/5peUP1rr30Dl1X7bvoL1y\nBfp7fknO/m7ZlQSv6Bw7VPVMxS8rV5Q9Txfo7zlWrL/xSeZSjrR/kCyRu01/CO71Z61Vu13aZj9w\nrF7nt9uIyM4pi9sfEBuJO9jatPwHCrsMbobiyJR+TFaoQC/Pr2xKdt6lXvaWPO+y9ae9QgTydH4l\ntW7SxPv6E/abCuFjN3MI5On8OkJfYw9wWqb6ZcUkTT+f46uZXQECeTq/PhGVjfgm/7HgLk1bUkdB\nwH7D22BLT3/jZxM72GYU2298kjn/cOkg24jHHxWLdJB+nXIUH9T0Jf5nqx/wp639Ai1Wo9hV0/Yg\nitmKVYp91Tba73eiarv/jlLBAn04vzJq6dy0S303a31T/XVerM/JczLjMjSWv0C+zi+T0rr+VdUI\nxffKX4497IlAvs4v/f26UTFO0aDvPVrxnqan9WQb6FO+Ank8v+wf0B9SJM+pEzS9sHzl2LOeCuTr\nHNP3FeVvfF4a7p8JOtD2P5aXFHavW8xv/olqu2/ORtcmKlYoztP/fD61+VrGnpr0DUVEYZdlPua3\n/53qaxR2/9xyxaWaZ/9iRKlQgT6eX8vENVxRrfhccZLOo4VaV6OmpyvqFHbOfVft9j8xSoUK5Ov8\nEt8mhd0L/K6izef8nU6vP1QoLbstgXydX/b3KwmqdTZo+mG17Q9yZQvk8/zSunaR5l8UIxXrFZfp\nHLPfbpQKFsjzOVZ0v/FJ5ir45GbXEUAAAQQQQAABBBBAoHQFuMyydI8dW44AAggggAACCCCAAAIV\nLEAyV8EHn11HAAEEEEAAAQQQQACB0hUgmSvdY8eWI4AAAggggAACCCCAQAULkMxV8MFn1xFAAAEE\nEEAAAQQQQKB0BUjmSvfYseUIIIAAAggggAACCCBQwQIkcxV88Nl1BBBAoJIF9LhqKy8rTk06aPp8\nxeOV7MK+I4AAAgiUjgCvJiidY8WWIoAAAgjkWUCJm73n7C7FIYqQ4k3FKXo31Qe9/SqtK6Tlor1d\njv4IIIAAAgj0VYBkrq9yLIcAAgggUBYCSsL+XTvSpBji1/bi4QMUVYqfKkF7QH0aNG0vI7Y+Vr6j\n9lfVPk3T/6xYozhYbft6cykIIIAAAggUQIBkrgDIfAUCCCCAQPEKKCGzBG2eol3xsOIdJWW3qn2k\npmcrbNQuroipvVXte2r6r5pu9JO5R/R5f31eqpqCAAIIIIBAwQTsXx0pCCCAAAIIVKyAkrAmJWV3\nCmCL4nzFGfr8Qx+kVvVExWrF79R+sGq7lHKSP9+q2SRyKRpMIoAAAggUTIBkrmDUfBECCCCAQBEL\nxLRtFgHFuUrOFqduq5K4n+rzWsVBCnt4WGvKfLtEk4IAAggggEDBBXiaZcHJ+UIEEEAAgSIWeELb\n9l0lb5bUOVV2iaWVEYo1SvIs4fu6wh6WQkEAAQQQQGBQBUjmBpWfL0cAAQQQKDKBn2l7woq3lMi9\nrdo+W/m94hK1vabaLrFkNM6HoUIAAQQQGDwBHoAyePZ8MwIIIIAAAggggAACCCDQZwFG5vpMx4II\nIIAAAggggAACCCCAwOAJkMwNnj3fjAACCCCAAAIIIIAAAgj0WYBkrs90LIgAAggggAACCCCAAAII\nDJ4Aydzg2fPNCCCAAAIIIIAAAggggECfBUjm+kzHgggggAACCCCAAAIIIIDA4AmQzA2ePd+MAAII\nIIAAAggggAACCPRZgGSuz3QsiAACCCCAAAIIIIAAAggMngDJ3ODZ880IIIAAAggggAACCCCAQJ8F\n/j9R41TSds79CwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 1500x300 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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PRLewOCFzN624q9ZEyyRj2bhXs3KV7k4btvoqEVyg/693cjdRJ3+VSZ3/O/cgPuV5OdGr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\nEEAAgdQC/x/SVp6fqDrkNQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 1500x300 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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HSuvR5vLM+bx3abYjeGZ2S/CMts2B943bp1G5feEUy3inK4c1Ymez85TNbWtDL5ars12p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\n",
            "text/plain": [
              "\u003cFigure size 1500x300 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for col in [\"chargingStationsUnderConstruction\", \"chargingStations\", \"pcInUse\", \"totalPcData\"]:\n",
        "    plt.plot(bptk_sd_env.state.obs_timeseries[col], lw=4, c=np.random.rand(3))\n",
        "    plt.title(col, fontsize=18)\n",
        "    plt.xlabel(\"Year\")\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "height": 929
        },
        "executionInfo": {
          "elapsed": 719,
          "status": "ok",
          "timestamp": 1698183374571,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "Q2yCi1Doj6da",
        "outputId": "543f8c7a-bfa5-4782-9eae-882e9ffbaba6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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EIAABCEAAAhBYFgL27FvnZj37drcMnEbgkhl9q26tcs0afWs+tdk1nsiqk8ty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\ngg==\n",
            "text/plain": [
              "\u003cFigure size 1500x300 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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Nw+75x4rsMaDBtwVna6+4d3S77rntZeIC8Ji5mnZBCocABCAAAQhAAAIQgAAEKiFwbFfK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\nmDSB/w86psxUwtcDmAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 1500x300 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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kpO0qdUBirW5MCCCAAAIIIJAZAr4JXGacJmeBAAIIpIdA06Eub2y255S0vbym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\n",
            "text/plain": [
              "\u003cFigure size 1500x300 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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7L5Jr/0k4A0iIIIUhAIGRJGAOUo5dOhFaS9c7Unf44nGtL4r/u3S4/zbFck7p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\nRK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 1500x300 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for col in [\"charging_stations_under_construction\", \"charging_stations\", \"pc_in_use\", \"total_pc_data\"]:\n",
        "    plt.plot(pysd_sd_env.state.obs_timeseries[col], lw=4, c=np.random.rand(3))\n",
        "    plt.title(col, fontsize=18)\n",
        "    plt.xlabel(\"Year\")\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rRv4SuQMjvZ7"
      },
      "source": [
        "## Model info (BPTK)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RLMMUFzc1CuU"
      },
      "outputs": [],
      "source": [
        "from sd_gym import util"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "height": 424
        },
        "executionInfo": {
          "elapsed": 898,
          "status": "ok",
          "timestamp": 1698183448553,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "hQt1UcdMREqe",
        "outputId": "f3d5ee35-9f60-480b-8936-f1bc95009c01"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  \u003cdiv id=\"df-8efe7ce5-4105-4b41-8ab3-5dd0743e1b84\" class=\"colab-df-container\"\u003e\n",
              "    \u003cdiv\u003e\n",
              "\u003cstyle scoped\u003e\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\u003c/style\u003e\n",
              "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
              "  \u003cthead\u003e\n",
              "    \u003ctr style=\"text-align: right;\"\u003e\n",
              "      \u003cth\u003e\u003c/th\u003e\n",
              "      \u003cth\u003eVariable Name\u003c/th\u003e\n",
              "      \u003cth\u003eObservable\u003c/th\u003e\n",
              "      \u003cth\u003eActionable\u003c/th\u003e\n",
              "      \u003cth\u003eType\u003c/th\u003e\n",
              "      \u003cth\u003eEquation\u003c/th\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/thead\u003e\n",
              "  \u003ctbody\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eecSales\u003c/th\u003e\n",
              "      \u003ctd\u003eecSales\u003c/td\u003e\n",
              "      \u003ctd\u003eYes\u003c/td\u003e\n",
              "      \u003ctd\u003eNo\u003c/td\u003e\n",
              "      \u003ctd\u003eflow\u003c/td\u003e\n",
              "      \u003ctd\u003eOverall_demand_on_cars*Relative_attractiveness...\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eecScrapping\u003c/th\u003e\n",
              "      \u003ctd\u003eecScrapping\u003c/td\u003e\n",
              "      \u003ctd\u003eYes\u003c/td\u003e\n",
              "      \u003ctd\u003eNo\u003c/td\u003e\n",
              "      \u003ctd\u003eflow\u003c/td\u003e\n",
              "      \u003ctd\u003eEC_in_use/Average_car_lifetime\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003epcSales\u003c/th\u003e\n",
              "      \u003ctd\u003epcSales\u003c/td\u003e\n",
              "      \u003ctd\u003eYes\u003c/td\u003e\n",
              "      \u003ctd\u003eNo\u003c/td\u003e\n",
              "      \u003ctd\u003eflow\u003c/td\u003e\n",
              "      \u003ctd\u003eRelative_attractiveness_of_PC*Overall_demand_o...\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003epcScrapping\u003c/th\u003e\n",
              "      \u003ctd\u003epcScrapping\u003c/td\u003e\n",
              "      \u003ctd\u003eYes\u003c/td\u003e\n",
              "      \u003ctd\u003eNo\u003c/td\u003e\n",
              "      \u003ctd\u003eflow\u003c/td\u003e\n",
              "      \u003ctd\u003ePC_in_use/Average_car_lifetime\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eoilProduction\u003c/th\u003e\n",
              "      \u003ctd\u003eoilProduction\u003c/td\u003e\n",
              "      \u003ctd\u003eNo\u003c/td\u003e\n",
              "      \u003ctd\u003eYes\u003c/td\u003e\n",
              "      \u003ctd\u003eflow\u003c/td\u003e\n",
              "      \u003ctd\u003e\"Average_petrol_consumption_(non-passenger_car...\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e...\u003c/th\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "      \u003ctd\u003e...\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003epriceOf1LFuel\u003c/th\u003e\n",
              "      \u003ctd\u003epriceOf1LFuel\u003c/td\u003e\n",
              "      \u003ctd\u003eYes\u003c/td\u003e\n",
              "      \u003ctd\u003eNo\u003c/td\u003e\n",
              "      \u003ctd\u003eaux\u003c/td\u003e\n",
              "      \u003ctd\u003eFuel_price*Random_market_situations\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003epricePcWithoutTaxes\u003c/th\u003e\n",
              "      \u003ctd\u003epricePcWithoutTaxes\u003c/td\u003e\n",
              "      \u003ctd\u003eNo\u003c/td\u003e\n",
              "      \u003ctd\u003eYes\u003c/td\u003e\n",
              "      \u003ctd\u003eaux\u003c/td\u003e\n",
              "      \u003ctd\u003e350000\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003epriceEcWithoutTaxes\u003c/th\u003e\n",
              "      \u003ctd\u003epriceEcWithoutTaxes\u003c/td\u003e\n",
              "      \u003ctd\u003eNo\u003c/td\u003e\n",
              "      \u003ctd\u003eYes\u003c/td\u003e\n",
              "      \u003ctd\u003eaux\u003c/td\u003e\n",
              "      \u003ctd\u003e450000\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eoverpaymEffect\u003c/th\u003e\n",
              "      \u003ctd\u003eoverpaymEffect\u003c/td\u003e\n",
              "      \u003ctd\u003eYes\u003c/td\u003e\n",
              "      \u003ctd\u003eNo\u003c/td\u003e\n",
              "      \u003ctd\u003eaux\u003c/td\u003e\n",
              "      \u003ctd\u003eEffect_of_infrastructure/Charging_stations_per...\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eyearToTurnOffVatPolicy\u003c/th\u003e\n",
              "      \u003ctd\u003eyearToTurnOffVatPolicy\u003c/td\u003e\n",
              "      \u003ctd\u003eNo\u003c/td\u003e\n",
              "      \u003ctd\u003eYes\u003c/td\u003e\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "    (() =\u003e {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\u003c/div\u003e\n",
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            ],
            "text/plain": [
              "                                 Variable Name Observable  ...  Type                                           Equation\n",
              "ecSales                                ecSales        Yes  ...  flow  Overall_demand_on_cars*Relative_attractiveness...\n",
              "ecScrapping                        ecScrapping        Yes  ...  flow                     EC_in_use/Average_car_lifetime\n",
              "pcSales                                pcSales        Yes  ...  flow  Relative_attractiveness_of_PC*Overall_demand_o...\n",
              "pcScrapping                        pcScrapping        Yes  ...  flow                     PC_in_use/Average_car_lifetime\n",
              "oilProduction                    oilProduction         No  ...  flow  \"Average_petrol_consumption_(non-passenger_car...\n",
              "...                                        ...        ...  ...   ...                                                ...\n",
              "priceOf1LFuel                    priceOf1LFuel        Yes  ...   aux                Fuel_price*Random_market_situations\n",
              "pricePcWithoutTaxes        pricePcWithoutTaxes         No  ...   aux                                             350000\n",
              "priceEcWithoutTaxes        priceEcWithoutTaxes         No  ...   aux                                             450000\n",
              "overpaymEffect                  overpaymEffect        Yes  ...   aux  Effect_of_infrastructure/Charging_stations_per...\n",
              "yearToTurnOffVatPolicy  yearToTurnOffVatPolicy         No  ...   aux                                               2015\n",
              "\n",
              "[62 rows x 5 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "parsed_model, info_df = util.sd_info(bptk_sd_env)\n",
        "info_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PSAB8eX6ZtDo"
      },
      "source": [
        "# RL Experiments using PySD"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mx7tvu7hg7LL"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "12SMwrCsAkNQ"
      },
      "outputs": [],
      "source": [
        "from sd_gym import core\n",
        "from sd_gym import env\n",
        "from sd_gym import rewards\n",
        "from sd_gym import wrappers\n",
        "import gym\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "09tUgwWDe7_1"
      },
      "outputs": [],
      "source": [
        "from acme import environment_loop\n",
        "from acme import specs\n",
        "from acme import wrappers as acme_wrappers\n",
        "from acme.agents import agent\n",
        "from acme.agents.tf import actors\n",
        "from acme.agents.tf import d4pg\n",
        "from acme.tf import networks\n",
        "from acme.tf import utils as tf2_utils\n",
        "from acme.utils import loggers\n",
        "import sonnet.v2 as snt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RnNz5OlK5GcK"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams[\"figure.figsize\"] = [15, 3]\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FDO3cCneb5ic"
      },
      "source": [
        "### Environment setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lNKyuQbtb5ir"
      },
      "outputs": [],
      "source": [
        "sd_model_filename = \"/tmp/electric_vehicles_norway.stmx\"\n",
        "\n",
        "# reward based on increase in fraction of attrativeness of ECs\n",
        "# reward_fn = rewards.ScalarDeltaReward('relative_attractiveness_of_ec',\n",
        "#                                       scaling_fn=lambda x: x*100)\n",
        "\n",
        "# reward based on increase in fraction of ECs in total car population\n",
        "reward_fn = rewards.ScalarDeltaReward(lambda obs: 100*obs['ec_in_use']/(obs['ec_in_use']+obs['pc_in_use']))\n",
        "\n",
        "env_params = core.Params(sd_model_filename,\n",
        "                         env_dt=1.0,\n",
        "                         sd_dt=.1,\n",
        "                         sd_units_types={\n",
        "                             'years': np.int32,\n",
        "                             'barrel': np.int64,\n",
        "                             },\n",
        "                         default_sd_units_limits={\n",
        "                             'nok/cars': (0, 100000),\n",
        "                         },\n",
        "                         sd_var_limits_override={\n",
        "                             'km_per_one_battery': (10, 1000),\n",
        "                             'kwh_per_battery': (10, 100),\n",
        "                             'electricity_price': (1, 10),\n",
        "                             'price_pc_without_taxes': (10000, 70000),\n",
        "                             'price_ec_without_taxes': (20000, None),\n",
        "                             'gov_policy_on_taxes': (0, 1),\n",
        "                             'oil_industry_capacity': (1000000, 2000000),\n",
        "                             },\n",
        "                         categorical_sd_vars={\n",
        "                             'average_car_lifetime': [1, 3, 5, 7, 10, 15],\n",
        "                             'vat': [0.15, 0.3, 0.44, 0.5],\n",
        "                         },\n",
        "                         actionables=[\n",
        "                             'average_car_lifetime', # how often cars are taken off the road\n",
        "                             'km_per_one_battery', # range of electric cars\n",
        "                             'kwh_per_battery', # charge for battery\n",
        "                             'electricity_price', # cost of electricity\n",
        "                             'price_ec_without_taxes', # price of electric cars\n",
        "                             'price_pc_without_taxes', # price of petrol cars\n",
        "                             'vat', # tax on purchases\n",
        "                             'gov_policy_on_taxes', # government tax incentives for adoption (fraction of tax)\n",
        "                             'oil_industry_capacity', # oil production capacity\n",
        "                             ],\n",
        "                         # observables=['pc_in_use', 'ec_in_use'],\n",
        "                         reward_function=reward_fn,\n",
        "                         simulator=\"PySD\")\n",
        "env = wrappers.make_sd_env(env_params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "executionInfo": {
          "elapsed": 19,
          "status": "ok",
          "timestamp": 1698183556541,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "-Q9VawwO--3s",
        "outputId": "a8f83bee-f397-467b-f14f-82680489440b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "actions: BoundedArray(shape=(9,), dtype=dtype('float64'), name='action', minimum=[-1. -1. -1. -1. -1. -1. -1. -1. -1.], maximum=[1. 1. 1. 1. 1. 1. 1. 1. 1.])\n",
            "observations: BoundedArray(shape=(53,), dtype=dtype('float64'), name='observation', minimum=[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n",
            " -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n",
            " -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n",
            " -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf], maximum=[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n",
            " inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n",
            " inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf])\n",
            "rewards: Array(shape=(), dtype=dtype('float64'), name='reward')\n",
            "discounts: BoundedArray(shape=(), dtype=dtype('float64'), name='discount', minimum=0.0, maximum=1.0)\n"
          ]
        }
      ],
      "source": [
        "environment = acme_wrappers.GymWrapper(env)\n",
        "environment_spec = specs.make_environment_spec(environment)\n",
        "\n",
        "print('actions:', environment_spec.actions)\n",
        "print('observations:', environment_spec.observations)\n",
        "print('rewards:', environment_spec.rewards)\n",
        "print('discounts:', environment_spec.discounts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GX5Hezp9ZAkY"
      },
      "source": [
        "### D4PG Agent training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8YybuKM7Ytaz"
      },
      "outputs": [],
      "source": [
        "# Get total number of action dimensions from action spec.\n",
        "num_dimensions = np.prod(environment_spec.actions.shape, dtype=int)\n",
        "\n",
        "# Create the shared observation network; here simply a state-less operation.\n",
        "observation_network = tf2_utils.batch_concat\n",
        "\n",
        "# Create the deterministic policy network.\n",
        "policy_network = snt.Sequential([\n",
        "    networks.LayerNormMLP((256, 256, 256), activate_final=True),\n",
        "    networks.NearZeroInitializedLinear(num_dimensions),\n",
        "    networks.TanhToSpec(environment_spec.actions),\n",
        "])\n",
        "\n",
        "# Create the distributional critic network.\n",
        "critic_network = snt.Sequential([\n",
        "    # The multiplexer concatenates the observations/actions.\n",
        "    networks.CriticMultiplexer(),\n",
        "    networks.LayerNormMLP((512, 512, 256), activate_final=True),\n",
        "    networks.DiscreteValuedHead(vmin=-150., vmax=150., num_atoms=51),\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pg9Zpx8v4mEn"
      },
      "outputs": [],
      "source": [
        "simple_agent = actors.FeedForwardActor(policy_network)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TDumgWi648EZ"
      },
      "outputs": [],
      "source": [
        "agent_logger = loggers.TerminalLogger(print_fn=print)\n",
        "env_loop_logger = loggers.InMemoryLogger()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iJtpM1zXY7-A"
      },
      "outputs": [],
      "source": [
        "# Create the D4PG agent.\n",
        "d4pg_agent = d4pg.D4PG(\n",
        "    environment_spec=environment_spec,\n",
        "    policy_network=policy_network,\n",
        "    critic_network=critic_network,\n",
        "    observation_network=observation_network,\n",
        "    sigma=0.7,\n",
        "    target_update_period=5,\n",
        "    min_replay_size=5,\n",
        "    n_step=5,\n",
        "    logger=agent_logger,\n",
        "    checkpoint=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "executionInfo": {
          "elapsed": 244656,
          "status": "ok",
          "timestamp": 1698184010446,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "v66m9280lxLJ",
        "outputId": "8ccf25ff-edb5-48fa-fa43-3a2c364d4908"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Critic Loss = 3.909 | Policy Loss = 0.000 | Steps = 1 | Walltime = 0\n",
            "Critic Loss = 3.824 | Policy Loss = 0.000 | Steps = 2 | Walltime = 2.002\n",
            "Critic Loss = 3.799 | Policy Loss = 0.000 | Steps = 3 | Walltime = 4.564\n",
            "Critic Loss = 3.758 | Policy Loss = 0.000 | Steps = 4 | Walltime = 5.004\n",
            "Critic Loss = 3.703 | Policy Loss = 0.000 | Steps = 5 | Walltime = 6.984\n",
            "Critic Loss = 3.532 | Policy Loss = 0.000 | Steps = 6 | Walltime = 9.675\n",
            "Critic Loss = 3.514 | Policy Loss = 0.000 | Steps = 7 | Walltime = 11.727\n",
            "Critic Loss = 3.501 | Policy Loss = 0.000 | Steps = 8 | Walltime = 13.718\n",
            "Critic Loss = 3.496 | Policy Loss = 0.000 | Steps = 9 | Walltime = 14.160\n",
            "Critic Loss = 3.498 | Policy Loss = 0.000 | Steps = 10 | Walltime = 16.908\n",
            "Critic Loss = 3.459 | Policy Loss = 0.000 | Steps = 11 | Walltime = 18.854\n",
            "Critic Loss = 3.349 | Policy Loss = 0.000 | Steps = 12 | Walltime = 21.535\n",
            "Critic Loss = 3.316 | Policy Loss = 0.000 | Steps = 13 | Walltime = 23.444\n",
            "Critic Loss = 3.374 | Policy Loss = 0.000 | Steps = 14 | Walltime = 23.854\n",
            "Critic Loss = 3.215 | Policy Loss = 0.000 | Steps = 15 | Walltime = 25.829\n",
            "Critic Loss = 3.291 | Policy Loss = 0.000 | Steps = 16 | Walltime = 28.443\n",
            "Critic Loss = 3.342 | Policy Loss = 0.000 | Steps = 17 | Walltime = 30.380\n",
            "Critic Loss = 3.197 | Policy Loss = 0.000 | Steps = 18 | Walltime = 33.112\n",
            "Critic Loss = 3.215 | Policy Loss = 0.000 | Steps = 19 | Walltime = 33.559\n",
            "Critic Loss = 3.235 | Policy Loss = 0.000 | Steps = 20 | Walltime = 35.488\n",
            "Critic Loss = 3.188 | Policy Loss = 0.000 | Steps = 21 | Walltime = 37.437\n",
            "Critic Loss = 3.197 | Policy Loss = 0.000 | Steps = 22 | Walltime = 40.084\n",
            "Critic Loss = 3.135 | Policy Loss = 0.000 | Steps = 23 | Walltime = 42.020\n",
            "Critic Loss = 3.212 | Policy Loss = 0.000 | Steps = 24 | Walltime = 42.453\n",
            "Critic Loss = 3.176 | Policy Loss = 0.000 | Steps = 25 | Walltime = 45.095\n",
            "Critic Loss = 3.180 | Policy Loss = 0.000 | Steps = 26 | Walltime = 47.018\n",
            "Critic Loss = 3.141 | Policy Loss = 0.000 | Steps = 27 | Walltime = 48.963\n",
            "Critic Loss = 3.203 | Policy Loss = 0.000 | Steps = 28 | Walltime = 51.650\n",
            "Critic Loss = 3.106 | Policy Loss = 0.000 | Steps = 29 | Walltime = 52.079\n",
            "Critic Loss = 3.144 | Policy Loss = 0.000 | Steps = 30 | Walltime = 53.986\n",
            "Critic Loss = 3.135 | Policy Loss = 0.000 | Steps = 31 | Walltime = 56.589\n",
            "Critic Loss = 2.994 | Policy Loss = 0.000 | Steps = 32 | Walltime = 58.513\n",
            "Critic Loss = 3.080 | Policy Loss = 0.000 | Steps = 33 | Walltime = 60.453\n",
            "Critic Loss = 3.093 | Policy Loss = 0.000 | Steps = 34 | Walltime = 60.880\n",
            "Critic Loss = 3.016 | Policy Loss = 0.000 | Steps = 35 | Walltime = 63.555\n",
            "Critic Loss = 3.043 | Policy Loss = 0.000 | Steps = 36 | Walltime = 65.459\n",
            "Critic Loss = 3.153 | Policy Loss = 0.000 | Steps = 37 | Walltime = 68.027\n",
            "Critic Loss = 3.025 | Policy Loss = 0.000 | Steps = 38 | Walltime = 69.922\n",
            "Critic Loss = 3.055 | Policy Loss = 0.000 | Steps = 39 | Walltime = 70.348\n",
            "Critic Loss = 3.096 | Policy Loss = 0.000 | Steps = 40 | Walltime = 72.230\n",
            "Critic Loss = 3.021 | Policy Loss = 0.000 | Steps = 41 | Walltime = 74.845\n",
            "Critic Loss = 3.061 | Policy Loss = 0.000 | Steps = 42 | Walltime = 76.774\n",
            "Critic Loss = 3.068 | Policy Loss = 0.000 | Steps = 43 | Walltime = 79.378\n",
            "Critic Loss = 3.082 | Policy Loss = 0.000 | Steps = 44 | Walltime = 79.799\n",
            "Critic Loss = 3.025 | Policy Loss = 0.000 | Steps = 45 | Walltime = 81.760\n",
            "Critic Loss = 3.013 | Policy Loss = 0.000 | Steps = 46 | Walltime = 83.685\n",
            "Critic Loss = 3.052 | Policy Loss = 0.000 | Steps = 47 | Walltime = 86.338\n",
            "Critic Loss = 3.037 | Policy Loss = 0.000 | Steps = 48 | Walltime = 88.256\n",
            "Critic Loss = 2.997 | Policy Loss = 0.000 | Steps = 49 | Walltime = 88.682\n",
            "Critic Loss = 3.053 | Policy Loss = 0.000 | Steps = 50 | Walltime = 91.362\n",
            "Critic Loss = 2.990 | Policy Loss = 0.000 | Steps = 51 | Walltime = 93.295\n",
            "Critic Loss = 2.957 | Policy Loss = 0.000 | Steps = 52 | Walltime = 95.232\n",
            "Critic Loss = 2.990 | Policy Loss = 0.000 | Steps = 53 | Walltime = 97.883\n",
            "Critic Loss = 3.058 | Policy Loss = 0.000 | Steps = 54 | Walltime = 98.318\n",
            "Critic Loss = 3.035 | Policy Loss = 0.000 | Steps = 55 | Walltime = 100.249\n",
            "Critic Loss = 3.021 | Policy Loss = 0.000 | Steps = 56 | Walltime = 102.823\n",
            "Critic Loss = 3.001 | Policy Loss = 0.000 | Steps = 57 | Walltime = 104.728\n",
            "Critic Loss = 2.933 | Policy Loss = 0.000 | Steps = 58 | Walltime = 106.648\n",
            "Critic Loss = 2.998 | Policy Loss = 0.000 | Steps = 59 | Walltime = 107.086\n",
            "Critic Loss = 2.893 | Policy Loss = 0.000 | Steps = 60 | Walltime = 109.745\n",
            "Critic Loss = 3.082 | Policy Loss = 0.000 | Steps = 61 | Walltime = 111.650\n",
            "Critic Loss = 3.005 | Policy Loss = 0.000 | Steps = 62 | Walltime = 114.250\n",
            "Critic Loss = 2.943 | Policy Loss = 0.000 | Steps = 63 | Walltime = 116.133\n",
            "Critic Loss = 2.979 | Policy Loss = 0.000 | Steps = 64 | Walltime = 116.564\n",
            "Critic Loss = 2.983 | Policy Loss = 0.000 | Steps = 65 | Walltime = 118.463\n",
            "Critic Loss = 2.927 | Policy Loss = 0.000 | Steps = 66 | Walltime = 121.096\n",
            "Critic Loss = 2.971 | Policy Loss = 0.000 | Steps = 67 | Walltime = 123.002\n",
            "Critic Loss = 2.984 | Policy Loss = 0.000 | Steps = 68 | Walltime = 125.609\n",
            "Critic Loss = 2.962 | Policy Loss = 0.000 | Steps = 69 | Walltime = 126.023\n",
            "Critic Loss = 2.993 | Policy Loss = 0.000 | Steps = 70 | Walltime = 127.909\n",
            "Critic Loss = 2.910 | Policy Loss = 0.000 | Steps = 71 | Walltime = 129.822\n",
            "Critic Loss = 2.995 | Policy Loss = 0.000 | Steps = 72 | Walltime = 132.500\n",
            "Critic Loss = 3.072 | Policy Loss = 0.000 | Steps = 73 | Walltime = 134.397\n",
            "Critic Loss = 2.954 | Policy Loss = 0.000 | Steps = 74 | Walltime = 134.811\n",
            "Critic Loss = 2.922 | Policy Loss = 0.000 | Steps = 75 | Walltime = 137.431\n",
            "Critic Loss = 2.944 | Policy Loss = 0.000 | Steps = 76 | Walltime = 139.337\n",
            "Critic Loss = 2.940 | Policy Loss = 0.000 | Steps = 77 | Walltime = 141.248\n",
            "Critic Loss = 2.969 | Policy Loss = 0.000 | Steps = 78 | Walltime = 143.913\n",
            "Critic Loss = 2.970 | Policy Loss = 0.000 | Steps = 79 | Walltime = 144.339\n",
            "Critic Loss = 2.915 | Policy Loss = 0.000 | Steps = 80 | Walltime = 146.246\n",
            "Critic Loss = 2.985 | Policy Loss = 0.000 | Steps = 81 | Walltime = 148.839\n",
            "Critic Loss = 2.985 | Policy Loss = 0.000 | Steps = 82 | Walltime = 150.771\n",
            "Critic Loss = 2.940 | Policy Loss = 0.000 | Steps = 83 | Walltime = 152.692\n",
            "Critic Loss = 2.936 | Policy Loss = 0.000 | Steps = 84 | Walltime = 153.127\n",
            "Critic Loss = 2.979 | Policy Loss = 0.000 | Steps = 85 | Walltime = 155.797\n",
            "Critic Loss = 2.914 | Policy Loss = 0.000 | Steps = 86 | Walltime = 157.810\n",
            "Critic Loss = 2.997 | Policy Loss = 0.000 | Steps = 87 | Walltime = 160.446\n",
            "Critic Loss = 2.874 | Policy Loss = 0.000 | Steps = 88 | Walltime = 162.360\n",
            "Critic Loss = 2.948 | Policy Loss = 0.000 | Steps = 89 | Walltime = 162.801\n",
            "Critic Loss = 2.874 | Policy Loss = 0.000 | Steps = 90 | Walltime = 164.713\n",
            "Critic Loss = 2.895 | Policy Loss = 0.000 | Steps = 91 | Walltime = 167.368\n",
            "Critic Loss = 2.882 | Policy Loss = 0.000 | Steps = 92 | Walltime = 169.397\n",
            "Critic Loss = 2.841 | Policy Loss = 0.000 | Steps = 93 | Walltime = 172.076\n",
            "Critic Loss = 2.914 | Policy Loss = 0.000 | Steps = 94 | Walltime = 172.502\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create an loop connecting this agent to the environment created above.\n",
        "env_loop = environment_loop.EnvironmentLoop(\n",
        "    environment, d4pg_agent, logger=env_loop_logger)\n",
        "\n",
        "env_loop.run(num_episodes=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "height": 295
        },
        "executionInfo": {
          "elapsed": 820,
          "status": "ok",
          "timestamp": 1698184016563,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "sCq2IZ8_l3B0",
        "outputId": "e48534b4-279c-434e-e01e-e58107a94ca8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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LbLyKnodhPg7jNV0ErxmTdohhMDuvYX4aosgEr+Huf2u9IVAbAhs2w3htF5ePADaIcbxU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\nbth4BeygYBJ59mGwVFB0Wa+mqnGko4kyXiHs1FB9rKpG6h4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n6oK+GpbDH6x13Ubfrset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\npyFgCBgChoAhYAh0HYH/DxRRKFjxpdTLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 1000x400 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "df = pd.DataFrame(env_loop_logger.data)\n",
        "plt.figure(figsize=(10, 4))\n",
        "plt.title('Training episodes returns')\n",
        "plt.xlabel('Training episodes')\n",
        "plt.ylabel('Episode return')\n",
        "plt.plot(df['episode_return']);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iCo2sePswKhI"
      },
      "source": [
        "### Evals"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qS3VXSNPwbYo"
      },
      "outputs": [],
      "source": [
        "def chart_state():\n",
        "  cols = [\"pc_in_use\", \"ec_in_use\"]\n",
        "  # cols = ['electricity_price', 'kwh_per_battery', 'km_per_one_battery']\n",
        "  plt.title('state', fontsize=18)\n",
        "  plt.xlabel(\"Year\")\n",
        "  plt.plot(env.state.obs_timeseries[cols], label=cols)\n",
        "  plt.legend()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q7Tqbe8dckpT"
      },
      "outputs": [],
      "source": [
        "def evaluate_agent_acme(agent: agent.Agent, num_steps=10):\n",
        "  # Run the actor in the environment for desired number of steps.\n",
        "  timestep = environment.reset()\n",
        "  total_reward = 0\n",
        "  for _ in range(num_steps):\n",
        "    action = agent.select_action(timestep.observation)\n",
        "    timestep = environment.step(action)\n",
        "    total_reward += timestep.reward\n",
        "  print('cumulative reward:', total_reward)\n",
        "  chart_state()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "height": 316
        },
        "executionInfo": {
          "elapsed": 2233,
          "status": "ok",
          "timestamp": 1698184462873,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "tMkYJVRlwL1_",
        "outputId": "d747efcd-0840-4fc4-d953-7b56d9ffb916"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cumulative reward: 59.41461148592202\n"
          ]
        },
        {
          "data": {
            "image/png": 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AvQRsqbz5PZ85c6bjt/yhhx6CKfh//OMfmDZtmuNnZe/evQE3YNu2bZgxYwbML7qN3jNS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\nYII=\n",
            "text/plain": [
              "\u003cFigure size 600x400 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "evaluate_agent_acme(d4pg_agent)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "height": 316
        },
        "executionInfo": {
          "elapsed": 2756,
          "status": "ok",
          "timestamp": 1698184469850,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "RkVO2aVomCpu",
        "outputId": "ef35928a-3a52-42e8-f6b6-061e6a028a37"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cumulative reward: 28.28019516128719\n"
          ]
        },
        {
          "data": {
            "image/png": 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Dij23OQhCwynxeb++FPX5Ha7siUA7IsD3YJyegOa56K9fvS9fKfSvLNzQ/LU2lxR057JB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\ngg==\n",
            "text/plain": [
              "\u003cFigure size 600x400 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "evaluate_agent_acme(simple_agent)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "last_runtime": {
        "build_target": "",
        "kind": "local"
      },
      "provenance": [
        {
          "timestamp": 1698184524057
        }
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
